{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e7abfb7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from copy import deepcopy\n",
    "from dataclasses import dataclass\n",
    "from typing import Optional, List, Tuple\n",
    "\n",
    "import numpy as np\n",
    "import numpy.random as rnd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "\n",
    "from alns import ALNS\n",
    "from alns.accept import SimulatedAnnealing\n",
    "from alns.select import AlphaUCB\n",
    "from alns.stop import MaxIterations, MaxRuntime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e92a956d",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1b9b14f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "SEED = 2345"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c74062fc",
   "metadata": {},
   "source": [
    "# The permutation flow shop problem\n",
    "This notebook implements ideas of [Stützle and Ruiz (2018)](https://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2018-006.pdf).\n",
    "\n",
    "In the permutation flow shop problem (PFSP), a set of jobs $N=\\{1, \\dots, n\\}$ has to be processed on a set of machines $M = \\{1, \\dots, m\\}$. Every job $j$ requires a processing time $p_{ij}$ on machine $i$. Moreover, all $n$ jobs have to be processed in the same order on every machine, and, the jobs follow the same machine order, starting from machine $1$ and ending at machine $m$. The objective is to find a permutation of the jobs, describing the order in which they are processed, such that the maximum completion time is minimized. This is also known as minimizing the *makespan*.\n",
    "\n",
    "In this notebook, we demonstrate how to use ALNS to solve PFSP, which is known to be NP-hard. We will also cover some advanced features of the `alns` package, including autofitting the acceptance criterion, adding local search to a repair operator, and using the `**kwargs` argument in `ALNS.iterate` to test different destroy rates.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe6fc311",
   "metadata": {},
   "source": [
    "## Data\n",
    "The [Taillard instances](http://mistic.heig-vd.ch/taillard/problemes.dir/ordonnancement.dir/ordonnancement.html) are the most used benchmark instances for the permutation flow shop problem. We will use the `tai_50_20_8` instance, which consists of 50 jobs and 20 machines.\n",
    "\n",
    "We use the [dataclass](https://docs.python.org/3/library/dataclasses.html#dataclasses.dataclass) decorator to simplify our class representation a little."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1e8b657b",
   "metadata": {},
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class Data:\n",
    "    n_jobs: int\n",
    "    n_machines: int\n",
    "    bkv: int # best known value\n",
    "    processing_times: np.ndarray\n",
    "    \n",
    "    @classmethod\n",
    "    def from_file(cls, path):\n",
    "        with open(path, 'r') as fi:\n",
    "            lines = fi.readlines()\n",
    "            \n",
    "            n_jobs, n_machines, _, bkv, _ = [int(num) for num in lines[1].split()]\n",
    "            processing_times = np.genfromtxt(lines[3:], dtype=int)\n",
    "            \n",
    "            return cls(n_jobs, n_machines, bkv, processing_times)\n",
    "    \n",
    "DATA = Data.from_file(\"data/tai50_20_8.txt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30a3416f-a8c5-4e5e-b808-94211c6629f8",
   "metadata": {},
   "source": [
    "Let's plot a Gantt chart of a random schedule."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "79f56d8c-e8c6-4436-aede-1b4aabf14c7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_completion_times(schedule):\n",
    "    \"\"\"\n",
    "    Compute the completion time for each job of the passed-in schedule.\n",
    "    \"\"\"\n",
    "    completion = np.zeros(DATA.processing_times.shape, dtype=int)\n",
    "\n",
    "    for idx, job in enumerate(schedule):\n",
    "        for machine in range(DATA.n_machines):\n",
    "            prev_job = completion[machine, schedule[idx - 1]] if idx > 0 else 0\n",
    "            prev_machine = completion[machine - 1, job] if machine > 0 else 0\n",
    "            processing = DATA.processing_times[machine, job]\n",
    "\n",
    "            completion[machine, job] = max(prev_job, prev_machine) + processing\n",
    "\n",
    "    return completion\n",
    "\n",
    "\n",
    "def compute_makespan(schedule):\n",
    "    \"\"\"\n",
    "    Returns the makespan, i.e., the maximum completion time.\n",
    "    \"\"\"\n",
    "    return compute_completion_times(schedule)[-1, schedule[-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "835f20ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot(schedule, name):\n",
    "    \"\"\"\n",
    "    Plots a Gantt chart of the schedule for the permutation flow shop problem.\n",
    "    \"\"\"\n",
    "    n_machines, n_jobs = DATA.processing_times.shape\n",
    "\n",
    "    completion = compute_completion_times(schedule)\n",
    "    start = completion - DATA.processing_times\n",
    "\n",
    "    # Plot each job using its start and completion time\n",
    "    cmap = cm.get_cmap(\"rainbow\", n_jobs)\n",
    "    machines, length, start_job, job_colors = zip(\n",
    "        *[\n",
    "            (i, DATA.processing_times[i, j], start[i, j], cmap(j - 1))\n",
    "            for i in range(n_machines)\n",
    "            for j in range(n_jobs)\n",
    "        ]\n",
    "    )\n",
    "\n",
    "    _, ax = plt.subplots(1, figsize=(12, 6))\n",
    "    ax.barh(machines, length, left=start_job, color=job_colors)\n",
    "\n",
    "    ax.set_title(f\"{name}\\n Makespan: {compute_makespan(schedule)}\") \n",
    "    ax.set_ylabel(f\"Machine\")\n",
    "    ax.set_xlabel(f\"Completion time\")\n",
    "    ax.set_yticks(range(DATA.n_machines))\n",
    "    ax.set_yticklabels(range(1, DATA.n_machines + 1))\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c98304b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(rnd.choice(range(DATA.n_jobs), size=DATA.n_jobs, replace=False), 'A random schedule')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90dc2e14-5785-4188-82a8-10649ab192c4",
   "metadata": {},
   "source": [
    "## Solution state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c9d39e8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Solution:\n",
    "    def __init__(\n",
    "        self, schedule: List[int], unassigned: Optional[List[int]] = None\n",
    "    ):\n",
    "        self.schedule = schedule\n",
    "        self.unassigned = unassigned if unassigned is not None else []\n",
    "\n",
    "    def objective(self):\n",
    "        return compute_makespan(self.schedule)\n",
    "\n",
    "    def insert(self, job: int, idx: int):\n",
    "        self.schedule.insert(idx, job)\n",
    "\n",
    "    def opt_insert(self, job: int):\n",
    "        \"\"\"\n",
    "        Optimally insert the job in the current schedule.\n",
    "        \"\"\"\n",
    "        idcs_costs = all_insert_cost(self.schedule, job)\n",
    "        idx, _ = min(idcs_costs, key=lambda idx_cost: idx_cost[1])\n",
    "        self.insert(job, idx)\n",
    "\n",
    "    def remove(self, job: int):\n",
    "        self.schedule.remove(job)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77d6cb77-e0a1-4348-9541-b2409325bec4",
   "metadata": {},
   "source": [
    "A key component in ruin-and-recreate algorithms is a fast way to compute the best insertion place for an unassigned job. In the PFSP, a naive approach is to try all $O(n)$ insertion positions and compute for each the resulting makespan in $O(nm)$ time. This has total time complexity $O(n^2m)$. A more efficient way is to use [Taillard's acceleration](https://scholar.google.com/citations?view_op=view_citation&hl=nl&user=vj-4SjYAAAAJ&citation_for_view=vj-4SjYAAAAJ:IjCSPb-OGe4C), which is a labeling algorithm that reduces this entire procedure to only $O(nm)$ time. We use this to implement `all_insert_cost`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6144cb0e-284b-4abd-981c-722002925d2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def all_insert_cost(schedule: List[int], job: int) -> List[Tuple[int, float]]:\n",
    "    \"\"\"\n",
    "    Computes all partial makespans when inserting a job in the schedule.\n",
    "    O(nm) using Taillard's acceleration. Returns a list of tuples of the\n",
    "    insertion index and the resulting makespan.\n",
    "\n",
    "    [1] Taillard, E. (1990). Some efficient heuristic methods for the\n",
    "    flow shop sequencing problem. European Journal of Operational Research,\n",
    "    47(1), 65-74.\n",
    "    \"\"\"\n",
    "    k = len(schedule) + 1\n",
    "    m = DATA.processing_times.shape[0]\n",
    "    p = DATA.processing_times\n",
    "\n",
    "    # Earliest completion of schedule[j] on machine i before insertion\n",
    "    e = np.zeros((m + 1, k))\n",
    "    for j in range(k - 1):\n",
    "        for i in range(m):\n",
    "            e[i, j] = max(e[i, j - 1], e[i - 1, j]) + p[i, schedule[j]]\n",
    "\n",
    "    # Duration between starting time and final makespan\n",
    "    q = np.zeros((m + 1, k))\n",
    "    for j in range(k - 2, -1, -1):\n",
    "        for i in range(m - 1, -1, -1):\n",
    "            q[i, j] = max(q[i + 1, j], q[i, j + 1]) + p[i, schedule[j]]\n",
    "\n",
    "    # Earliest relative completion time\n",
    "    f = np.zeros((m + 1, k))\n",
    "    for l in range(k):\n",
    "        for i in range(m):\n",
    "            f[i, l] = max(f[i - 1, l], e[i, l - 1]) + p[i, job]\n",
    "\n",
    "    # Partial makespan; drop the last (dummy) row of q\n",
    "    M = np.max(f + q, axis=0)\n",
    "\n",
    "    return [(idx, M[idx]) for idx in np.argsort(M)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27f9f2b2",
   "metadata": {},
   "source": [
    "## Destroy operators\n",
    "We implement two destroy operators: a random job removal operator, and an adjacent job removal operator. Both destroy operators rely on the `n_remove` parameter. We set a default value of 2 in case it's not provided. We will show later how we can experiment with different values for `n_remove`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9c1ea3da",
   "metadata": {},
   "outputs": [],
   "source": [
    "def random_removal(state: Solution, rnd_state, n_remove=2) -> Solution:\n",
    "    \"\"\"\n",
    "    Randomly remove a number jobs from the solution.\n",
    "    \"\"\"\n",
    "    destroyed = deepcopy(state)\n",
    "\n",
    "    for job in rnd_state.choice(DATA.n_jobs, n_remove, replace=False):\n",
    "        destroyed.unassigned.append(job)\n",
    "        destroyed.schedule.remove(job)\n",
    "\n",
    "    return destroyed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ff11a5d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def adjacent_removal(state: Solution, rnd_state, n_remove=2) -> Solution:\n",
    "    \"\"\"\n",
    "    Randomly remove a number adjacent jobs from the solution.\n",
    "    \"\"\"\n",
    "    destroyed = deepcopy(state)\n",
    "    \n",
    "    start = rnd_state.randint(DATA.n_jobs - n_remove)\n",
    "    jobs_to_remove = [state.schedule[start + idx] for idx in range(n_remove)]\n",
    "\n",
    "    for job in jobs_to_remove:\n",
    "        destroyed.unassigned.append(job)\n",
    "        destroyed.schedule.remove(job)\n",
    "\n",
    "    return destroyed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44207f2f",
   "metadata": {},
   "source": [
    "## Repair operator\n",
    "We implement a greedy repair operator and use a specific ordering for the unassigned jobs: the jobs with the highest total processing times are inserted first. This is also known as the *NEH ordering*, after [Nawaz, Enscore and Ham (1983)](https://www.sciencedirect.com/science/article/abs/pii/0305048383900889)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2bb06bab",
   "metadata": {},
   "outputs": [],
   "source": [
    "def greedy_repair(state: Solution, rnd_state, **kwargs) -> Solution:\n",
    "    \"\"\"\n",
    "    Greedily insert the unassigned jobs back into the schedule. The jobs are\n",
    "    inserted in non-decreasing order of total processing times.\n",
    "    \"\"\"\n",
    "    state.unassigned.sort(key=lambda j: sum(DATA.processing_times[:, j]))\n",
    "\n",
    "    while len(state.unassigned) != 0:\n",
    "        job = state.unassigned.pop()  # largest total processing time first\n",
    "        state.opt_insert(job)\n",
    "\n",
    "    return state"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "494a071c",
   "metadata": {},
   "source": [
    "## Initial solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "33eb4baf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def NEH(processing_times: np.ndarray) -> Solution:\n",
    "    \"\"\"\n",
    "    Schedules jobs in decreasing order of the total processing times.\n",
    "\n",
    "    [1] Nawaz, M., Enscore Jr, E. E., & Ham, I. (1983). A heuristic algorithm\n",
    "    for the m-machine, n-job flow-shop sequencing problem. Omega, 11(1), 91-95.\n",
    "    \"\"\"\n",
    "    largest_first = np.argsort(processing_times.sum(axis=0)).tolist()[::-1]\n",
    "    solution = Solution([largest_first.pop(0)], [])\n",
    "\n",
    "    for job in largest_first:\n",
    "        solution.opt_insert(job)\n",
    "\n",
    "    return solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7fc0f4d1-ffa2-4f71-ab09-cc4e6d9cc0a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial solution objective is 3929.\n",
      "This is 5.9% worse than the best known value 3709.\n"
     ]
    }
   ],
   "source": [
    "init = NEH(DATA.processing_times)\n",
    "objective = init.objective()\n",
    "pct_diff = 100 * (objective - DATA.bkv) / DATA.bkv\n",
    "\n",
    "print(f\"Initial solution objective is {objective}.\")\n",
    "print(f\"This is {pct_diff:.1f}% worse than the best known value {DATA.bkv}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f3c771d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(init.schedule, \"NEH\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8f2fc12",
   "metadata": {},
   "source": [
    "## Heuristic solutions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "67376595",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "alns = ALNS(rnd.RandomState(SEED))\n",
    "\n",
    "alns.add_destroy_operator(random_removal)\n",
    "alns.add_destroy_operator(adjacent_removal)\n",
    "\n",
    "alns.add_repair_operator(greedy_repair)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "52a42a27-e982-4b22-ba98-c6cff32733e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "ITERS = 8000\n",
    "\n",
    "init = NEH(DATA.processing_times)\n",
    "select = AlphaUCB(\n",
    "    scores=[5, 2, 1, 0.5],\n",
    "    alpha=0.05,\n",
    "    num_destroy=len(alns.destroy_operators),\n",
    "    num_repair=len(alns.repair_operators),\n",
    ")\n",
    "stop = MaxIterations(ITERS)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7870678-289f-40ad-bea7-ab61a43956de",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Autofitting acceptance criteria\n",
    "To use the simulated annealing criterion, we need to determine three parameters: 1) the start temperature, 2) the final temperature and 3) the updating step. These parameters are often calculated using the following procedure:\n",
    "\n",
    "- Start temperature is set to a specific value, such that a first candidate solution is accepted with 50% probability if it is 5% worse than the initial solution.\n",
    "- Final temperature is set to 1.\n",
    "- The updating step is set to the linear/exponential growth rate that is needed to decrease from the start temperature to the final temperature in the specified number of iterations.\n",
    "\n",
    "Because this is such a common procedure, the `SimulatedAnnealing` class exposes the `autofit` method that can determine these parameters automatically. See the [documentation](https://alns.readthedocs.io/en/latest/accept.html#alns.accept.SimulatedAnnealing.SimulatedAnnealing.autofit) for more information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "50d07c30",
   "metadata": {},
   "outputs": [],
   "source": [
    "accept = SimulatedAnnealing.autofit(init.objective(), 0.05, 0.50, ITERS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "96ae1785-9a55-4289-848a-b419adac4b57",
   "metadata": {},
   "outputs": [],
   "source": [
    "result = alns.iterate(init, select, accept, stop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "98099f7e-582e-4664-b3ed-2b32cc251dfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VmDZtmiKcAECjRo1w4MAByLKMJUuWKKnCzBg5ciQkScKUKVMCtOkpKSl44YUX8Pnnnyv+yADw448/YurUqQH+3CKSkpJw7bXXYsaMGejevXuA2fOwYcOwceNGzJo1CwCQnZ2NZ5991rJePxdeeCH+/PNPHDx4EACwePFibNiwIaDc5MmTcejQITz99NOK4Ld161bcd999Aa4QeoYNG4bff/8dq1atAuAz7VcvqPhp2LAhJElSFnnuueceRUjUI0kS3njjDfzxxx+YPn26sn/58uV46qmnlLHt3r07qlSpgrfeekt5RubPn4958+Zp6mvcuDHuv/9+/Pe//1X6mZeXh0ceeQTNmzdHamqq6TWGQo0aNVCuXDnlup9++mn8+OOPrjw/ADBw4EAkJCTglVdeUd6xTz75RLho4uQduOGGG3D++edjwoQJitn9jh078Oqrr+Kaa65Br1697A4BIYQQN4hc3D5CCCGljR07dsi33Xab3KpVK7l9+/Zy69at5V69esnTp0+XCwsLlXKnT5+W27dvL1eqVElu0KCB/OWXX8pdu3aVGzVqJDds2FATrd6PKIL7oUOH5JEjR8p169aV27VrJ3fs2FF+6aWXNG3Jsixv375dvvLKK+W6devK7du3l3v16iUvXrxYU2br1q1yly5d5BYtWsht2rSRZ8+eLS9fvlxu3769HBcXJ1eqVEmYpm7IkCGmEdH/97//yR07dpQbNWokd+zYUb7kkkvkv//+2+6QmkYPz83NlR955BG5Xr16ctu2beULL7xQiUper149+Z577pFnzJght2/fXgYg16hRQ+7Xr59yfnp6unz99dfLtWvXlrt27So/8cQT8vjx42UAcvv27eWvvvpKKfvXX3/JAwcOlOvUqSN36tRJ7tmzZ0BEdBHp6enybbfdJteuXVvu1KmTPGjQIHnixIkyALlJkybyCy+8oJSdMGGCXL9+fblNmzbylVdeKefk5JjW/dtvv8k9evSQ69evL3fq1Enu169fwH1dvny53LNnT7lmzZpyr1695FtvvVW+6KKL5Li4OLl9+/byP//8o5R966235JYtW8otWrSQ27ZtK48bN07JBPDrr79qxvH666+XZVmW+/XrJ1eqVElYn5qpU6fKLVu2VO7NiBEjlGPvvfee3KBBA7lt27Zy//79NRkcrJ6fMWPGyPXq1ZMByC1btpSHDRsW0PbPP/8sd+zYUa5Tp47cu3dv+cEHH5TPPfdcuXz58nL79u2VlHNO34HTp0/L9913n9ywYUO5efPmctOmTeWnn35aSSspy7J82WWXyTVq1FCeqcWLF8tfffWVZizU2SUIIYQEhyTLOuc6QgghJApp2LAh+vTpg48++ijSXSGEEEIIiSpoik8IISQqWblypcZ/PjMzkwG5CCGEEEIEULAnhBASlfz888+KT/6KFStw/PhxXHTRRRHuFSGEEEJI9EFTfEIIIVHJnDlz8OCDDyq/7733Xtx+++0R7BEhhBBCSHRCwZ4QQgghhBBCCCnB0BSfEEIIIYQQQggpwVCwJ4QQQgghhBBCSjCxke5AScDr9eLgwYNISUmBJEmR7g4hhBBCCCGEkFKOLMs4c+YMateuDY/HXCdPwd4GBw8eRL169SLdDUIIIYQQQgghZYx9+/ahbt26pmUo2NsgJSUFgG9AK1SoEOHeEEIIIYQQQggp7Zw+fRr16tVT5FEzKNjbwG9+X6FCBQr2hBBCCCGEEEKKDTvu4AyeRwghhBBCCCGElGAo2BNCCCGEEEIIISUYCvaEEEIIIYQQQkgJJmp87KdOnYp77rkHCxYsQJ8+fZT97733Ht5//30kJiaiYsWKeP/991GnTh3leIsWLVCzZk1NXddffz1uv/125feBAwdwxx134OTJk8jOzsbtt9+O0aNHh/2aCCGEEEIIISTSFBYWIj8/P9LdIALi4uIQExMTcj1RIdgfPHgQkydPDtj/7bffYtKkSfj7779RtWpVPP300xg6dChWr16t5PGrWbMmFi5caFi31+vF0KFDceWVV+KJJ57AsWPH0LZtW1SvXh3Dhw8P1yURQgghhBBCSMTJyMjA/v37IctypLtCBEiShLp16yI5OTmkeqJCsL/nnnvw+OOPB2jRn332Wdx8882oWrUqAGDs2LF45pln8NNPP2HYsGG26p49ezb++ecf/PHHHwCAatWq4aabbsJzzz1HwZ4QQgghhBBSaiksLMT+/fuRlJSEatWq2YquTooPWZZx7Ngx7N+/H+ecc05ImvuIC/azZs1CXFwcBg0apNmflpaGtWvX4oknnlD2paamolmzZpg7d65twX7evHlo3ry5ZgWkS5cumDx5Mk6ePIlKlSq5cyGEEEIIIYQQEkXk5+dDlmVUq1YN5cqVi3R3iIBq1aph9+7dyM/PL7mCfWZmJp544gnMmTMHubm5mmO7du0CANSoUUOzv2bNmsoxfx233HILduzYgZiYGAwcOBAPPvgg4uPjAQA7d+4U1uFvQyTY5+bmavpz+vTpEK6SEEIIIYQQQiIHNfXRi1v3JqJR8cePH4/Ro0ejVq1aAceysrIAAAkJCZr9CQkJyjEAaN68OcaMGYPFixdj5syZ+Pbbb3HDDTdo6hHVoW5DzwsvvIDU1FTlX7169YK7QEIIIYQQQgghJMxETLBfs2YNVqxYYRidPikpCQACNPm5ubnKMQCYMWMGzj33XABA9erVMXHiRHz99dfYvn27Uo+oDnUbeh577DGkp6cr//bt2xfEFRJCCCGEEEIIIeEnYqb4P/30E7Kzs3HhhRcCAHJycgAA9913HypWrKhEyT9y5IjmvMOHD2PAgAGG9TZp0gQA8O+//+Kcc85B48aNMX/+/IA6AKBRo0bCOhISEgK0/IQQQgghhBBCio+srCy88sor+P333xEbG6soeS+99FJcc801qF69eqS7aMqpU6cwZcoURcYNJxHT2I8fPx5r1qzBwoULsXDhQnzxxRcAgClTpmDhwoXo0qULOnbsiNWrVyvnnD59Gtu2bUP//v0BABs2bMAHH3ygqffAgQMAgPr16wMA+vXrh61btyIjI0Mps2rVKnTu3JmB8wghhBBCCCEkCsnKykLfvn2Rnp6OBQsWYMGCBVi6dCnuvvtuPPjgg3j77bcj3UVLTp06hUmTJuHUqVNhbyuiPvZWPPnkk/j4449x4sQJAMCbb76JNm3a4KKLLgIAnDhxAi+//DLS0tIAANnZ2XjppZfQt29ftGzZEgBw8cUXo3Xr1njrrbcAAMePH8cnn3yCxx9/PAJXRAghhBBCCCHEiqeeegp5eXl45ZVXEBtbZGh+2WWX4a677opgz6KTqBDs77vvPlx77bUB28OHD8f48eMxYMAAdO/eHcuWLcOsWbPg8fi63a5dO1x55ZUYMmQI+vTpgwsuuABNmjTBV199pUQXjImJwaxZs/Dnn3+iR48eGDhwIJ566inmsCeEEEIIIYSUKWRZRlZeQUT+ybJsu58FBQX4v//7P1x77bXCqPEPPvggrrzySvTp0weJiYn46KOPAAAzZ85EixYt0LBhQ6Xsrbfeipo1a+Kmm27Co48+in79+iEuLg4zZsxQzp88eTJGjBiBrl27QpIkRcM+efJkdOjQAb1790bv3r3xxx9/AAAyMjKUc19++WWMGDECXbp0wfnnn69kcNu0aZMi11577bXo06cPvvvuuyDumj0k2ckIl1FOnz6N1NRUpKeno0KFCpHuDiGEEEIIIYRYkpOTg127dqFRo0ZITExEVl4BWj01JyJ92fT0ICTF2wvxtnHjRrRt2xY//PADLrnkEtOyDRs2xMSJEzFy5EgAwEcffYSJEydi9+7dSpmRI0fihx9+wIIFC9ChQwdMmjQJnTt3xtChQ9GwYUNUqlQJCxYsQMWKFdG/f3988803mDlzJl577TUsX74cFStWxNKlS9GvXz9s2bIFDRo0UNquXr065s+fj+TkZAwfPhwpKSn4+OOPAQC7d+9Go0aNsGvXLs1igxr9PVLjRA6NCo09IYQQQgghhBACAOnp6QCA5ORk1+rs0KEDOnToAACYMGEChg4dqhy77LLLlOB2c+fORWpqKp577jnceuutyv7u3bujadOmATHehg0bpvSzT58+WLdunWt9dkLEouITQkom/x7LQEpCLKpXSLQuTAghhBBCooZycTHY9PSgiLVtF78wnZmZ6Vr7devWtX3szJkz2Lt3L6ZPn47Zs2cr+/Pz83HmzBlN2dq1ayvbKSkpOH36tEs9dgYFe0KIbY6eyUG/VxcBAHa/eHGEe0MIIYQQQpwgSZJtc/hI0qJFC6SmpmLTpk0YNmyYaVm9D35hYaGwXEyM8cKC0bFx48Zh1KhRpu2rz5UkyVEsATehKT4hxDbbj2RYFyKEEEIIISQEYmJicOedd+LLL78UHh82bBjGjRsHwKclV2vR/enPQyElJQX169fH1q1bNftnzpyJb775xnY9/qDvAOD1el21QAhoK2w1E0IIIYQQQgghQTBx4kQkJSXh4YcfRkFBAQBfVP+33noL//zzDx566CEAPt/5pUuXAvBFq581a5Yr7T/xxBP4+OOPsXfvXgDAsWPHMGnSJLRp08Z2HVWqVIHH48HJkyexatUqJcBfOIh+OwxCSNQQmGyEEEIIIYQQ90lISMDvv/+Ol19+Gb1790ZcXBxyc3PRunVrLFq0CDVq1ADgWwC4/vrr0bVrVzRt2hRXXnklJkyYgD59+mD27Nl48skn8euvvwKAsk8d7O7w4cN48cUXsWTJEk1gvNtvvx0ZGRkYPHgwqlSpgpiYGEyZMgXNmzcPODc+Ph4xMTF48cUXcfjwYfTr1w/z5s1D+fLlMW7cOIwYMQLJycl49dVXwzZeTHdnA6a7I8TH0h3Hcf0HKwDQx54QQgghJNoxS6VGogOmuyOEFD9U2RNCCCGEEBJ1ULAnhBBCCCGEEEJKMBTsCSG2kaiyJ4QQQgghJOqgYE8IIYQQQgghhJRgKNgTQgghhBBCCCElGAr2hBDbSLTEJ4QQQgghJOqgYE8IIYQQQgghhJRgKNgTUkLIL/Ri2oId2LA/PWJ9UCvsf95wKGL9IIQQQgghhBRBwZ6QEsLHS3dj8pytGDZ1SaS7AgAY89kaFBR6I90NQgghhBBCyjwU7AkpIWw6eDrSXQhAjnQHCCGEEEJIqWPlypXo06cPJElCixYt0KdPH3Tp0gVNmzbF448/jsLCQlfamThxInbv3u1KXZEmNtIdIISUHCRd9DzG0iOEEEIIIW7TtWtXLFy4EJIk4dFHH8XIkSMBAGvXrkW3bt1Qp04d3HXXXSG3M2nSJPTp0wcNGzYMua5IQ409IYQQQgghhJCop2PHjmjTpg3mz58f6a5EHdTYE+IC0//chdV7TmLKNR0QG1N21stoik8IIYQQUoKQZSA/KzJtxyW5kjs5Pz9fY0U6efJkfPbZZ0hNTQUAPPvss7jgggsAADt37sSYMWOQlZUFWZZRq1YtPPPMM6hWrRqGDx8OALjvvvtQsWJFXHPNNbjzzjtD7l+koGBPiAtMmrUJADC4TU0MbVc7wr0JH8xjTwghhBBSgsnPAp6P0Fz18YNAfPmQqpg9eza2bNmCF198EQDw/vvv48MPP8Ty5ctRsWJFLF26FP369cOWLVvQoEED3H333ejatSuefvppAMDNN9+MZcuWYeTIkYqp/5QpU9CnT59Qry7ilB3VIiHFQGZuQaS7EFb0cr1MlT0hhBBCCAkjL774Ivr06YO2bdvi6quvxqOPPoohQ4YAAJ577jnceuutqFixIgCge/fuaNq0KT744AMAwIEDB7Bv3z4l2N5zzz2HAQMGROQ6wg019oSUEKJRhpajsleEEEIIIURIXJJPcx6ptoNAHTzv+PHjGD58OC6//HLMmDEDe/fuxfTp0zF79mylfH5+Ps6cOQPAFxxvxIgRWLBgAa699lrccsstaNasWciXEo1QsCckSvF6ZXg8tH0nhBBCCCEuIUkhm8NHkqpVq+Lee+/FVVddhZ07dwIAxo0bh1GjRgnLX3bZZdi/fz+++OILfPDBB3j11Vfx1Vdf4bLLLivGXhcPNMUnxEXcMk2/9/O16P3KAmTnuZOj0y30PvY0xSeEEEIIIcVJbGys8n/9+vWxdetWzfGZM2fim2++AQB8/fXXSE1NxR133IG//voLl19+OT788EOlrDoIn1/LX1KhYE9IFPLj+oPYl5aN3zcfUfbJlKIJIYQQQkgZJi8vDx9//DGaNm2K5s2b44knnsDHH3+MvXv3AgCOHTuGSZMmoU2bNgCARx55BBs3blTOz8/P15jiV6tWDSdPnsTRo0dx4YUXFu/FuAxN8QmJYmiITwghhBBCyhorV67Eww8/DMAXPO+jjz5CYWEh0tLS0Lx5c/z888+IjY3F7bffjoyMDAwePBhVqlRBTEwMpkyZgubNmwMA7r33XowaNQrly5dHdnY2WrdujUmTJintPPnkk3jssceQmpqKxx57LCLX6hYU7AkhDtAuNSzcehSD29SKUF8IIYQQQkhppGvXrli4cKGtsg888AAeeOAB4bGxY8di7Nixhufec889uOeee4LpYtRBU3xCophozxs/esaaSHeBEEIIIYSQMg8Fe0IIIYQQQgghpARDwZ6QEkI0hM6LdgsCQgghhBBCyiIU7AkhtikojIblBUIIIYQQQogaCvaERDFSFMXF/3XjYVz93rJId4MQQgghhDiEaZOjF7fuDQV7QkoIkf4ej56xOrIdIIQQQgghjoiJiQHgy/9OohP/vfHfq2BhujtCCCGEEEIIKYXExsYiKSkJx44dQ1xcHDwe6nWjCa/Xi2PHjiEpKQmxsaGJ5hTsCQmRnPzCYmmHgesIIYQQQogTJElCrVq1sGvXLuzZsyfS3SECPB4P6tevDynEyT4Fe0JC5Mf1B8NWt/r9jrQpPiGEEEIIKXnEx8fjnHPOoTl+lBIfH++KJQUFe0JCJLfAG+kuEEIIIYQQYojH40FiYmKku0HCCJ0sCAkRtdGM20p1Wt8TQgghhBBCrKBgTxQycwsi3QViAi3xCSGEEEIIISIo2BMAwH+X7ELrCXPw9er9ke5KiYYadkIIIYQQQkhxQ8GeAACenr0JADDuq/UR7knJQxPgLnLdIIQQQgghhJRRKNgTEsUwxR0hhBBCCCHECgr2hISIRAN8QgghhBBCSAShYE9ICUGO4kT26Vn5WPbviajuIyGEEEIIIaUVCvaEuEhZ1N3LsowhbyzGdf+3HN+vOxDp7hBCCCGEEFLmoGBPSIiEN3he9C8VyDJwMD0HAPDLhsMR7g0hhBBCCCFlDwr2hJQQotXIPVr7RQghhBBCSFmBgj0hUUxJiIrvpV89IYQQQgghEYWCPSEhUgJk77Cilusp4hNCCCGEEFL8ULAnhISErBLnqbwnhBBCCCGk+KFgTwgJCQrzhBBCCCGERBYK9oSEwJq9J7H9aEbY6i8JZv4ns/Ii3QVCCCGEEELKNLGR7gAhJZWjZ3Iw/O2lrtcrG6nAo1QzfkUYxoAQQgghhBBiH2rsCQmSfWnZYam3pJm2+3PY+yhhnS+BpGfl4z8f/YXZfx+MdFcIIYQQQkiUQMGekKAJFGLdEMqNqpApNBMAr8/dhnlbjuLu/62NdFdIEMiyjH1pWcaWOYQQQgghQUDBnpAg8YZpXq6e8P++6Yhqf1GZNXtPhqdxEvWcyGRMg5LMe4t34oKXF+DFX7dEuiuEEEIIKUVQsCckSEQKN8nlaHdfrd4v3P/tGvF+UvopCQEViTEv/uIT6N9btBO5BYUR7g0hhBBCSgsU7AmxwGugmveGyZTW0BRfdSDW44HXK6MwXGYDJGpxe/GIRI6Pl+6OdBcIIYQQUkqgYE/w9/5Tke5C1PLeon/R8Znfsf3ImYBjYRPsDYPiy6oyMvq/tgj9Xl2InPzo0frRbZgQ++w5kRXpLhBCCCGklEDBnuCRbzZEugtRywu/bEF6dj6e+uGfwIMCIdad4HnWlRw5nYudxzOx+0QW9p8MT3R+Qkh4iY/ln2BCCCGEuANnFQTxMbTttaJQILEXtxW8ugsxmnsWPWry6OlJ6YVva+khITYm0l0ghBBCSCkhNtIdIJGh0Cvjzhmr0aZOKieXNhD5sotM8d3wfzY2xS8iRtUQ3ezLFhKd7EsN1NgTQgghxC04qyijLNx6FL9tOoLXft+GhDg+BlYUCKRnkTwdTh9zj0qei1H9oF87ISWTBAr2hBBCCHEJzirKKDn5XmWbk0trCgq9AfuKO3hevMqyQp3rPlz9CAY5ivpCSLTDby8hhBBC3IKzCkJTfBsI5dUwybBGwfPiVH71aguCaBLsSfihIX7pIS6Gf4IJIYQQ4g6cVZRR1G661BpZIxKei1ugjlWZ31dNTlC2KdcTUjJRu9QQQgghhIQCJTpCH3sbiIRnUdC6WBcm6obB81T7K5SLsyxPSimUBUsNccxIQgghhBCXiBqJburUqZAkCQsXLtTsf++999C5c2f06NEDF198MQ4cOKA5npeXh7Fjx+Lcc89F586dce+99yIvL09T5sCBAxg6dCh69OiBTp064d133w335ZQoSqspfqFXxper9mHnsYyQ6xJp57PzCwP2uaGBM5LT1QsJal92O3nvo428Ai++WLkX+9KyIt2VEodEyb7UQFN8QgghhLhFVMwqDh48iMmTJwfs//bbbzFp0iTMmTMHf/75J7p164ahQ4fC6y0KZDZu3Dhs3boVK1aswMqVK7F582aMGzdOOe71ejF06FCcf/75+PPPPzFnzhxMnDgR3377bbFcW7SiFg1Kqyn+l6v24eGv/8aFry4KuS6R6Lz3RGbAPlcEewMVvFqAVxeJpnR3drvy/uJ/8ei3G3DhqwvD2R1Cohqa4hNCCCHELaJCorvnnnvw+OOPB+x/9tlncfPNN6Nq1aoAgLFjx2Ljxo346aefAAAnTpzAu+++i/vvvx8xMTGIiYnB/fffj3fffRdpaWkAgNmzZ+Off/7B2LFjAQDVqlXDTTfdhOeee66Yri46mb50t7JdWgX7v3anuVaXSGNfPiE2YF84NfbfrimyVvFGICp+vIvaxT93nAAA5BdG0aoEIcUM3WgIIYQQ4hYRl+hmzZqFuLg4DBo0SLM/LS0Na9euxbnnnqvsS01NRbNmzTB37lwAwOLFi5Gfn68p06VLF+Tn52PRIp+Wdt68eWjevDmSk5M1ZdasWYOTJ0+G89KimpW7ioTehLjSaYq/7cgZ1+oSTcBF+8KlgTt6JkfzW2uWH5YmA5BcvDRPxL88JRc37wOJLCXRjYYQQggh0UmgyrEYyczMxBNPPIE5c+YgNzdXc2zXrl0AgBo1amj216xZUzm2c+dOxMbGokqVKsrxatWqISYmRlNGVIe/jUqVKgX0Kzc3V9Of06dPB3uJJQI3NbHRRH6Be5NmkXl8uKbkdhYRND72xSTZu9mMh9IpIdTYE0IIIcQ1IirRjR8/HqNHj0atWrUCjmVl+YJqJSQkaPYnJCQox7KyshAfHx9wbnx8vKaMqA51G3peeOEFpKamKv/q1avn8MpKFqVVxnLTRF3kx+6WQP3Rn7vw2u/bVBUHltHfIq0pvivdsMTOeNodEgr2wcORKz1QsCeEEEKIW0RMsF+zZg1WrFiB0aNHC48nJSUBQIAmPzc3VzmWlJQUEAEf8EXKV5cR1aFuQ89jjz2G9PR05d++ffscXFnJo7ROLtWXFaoQbneRIJhmJs7ahDfnbceOo77o/SLzXEknCMsaU/ziuYFuLpRQridllZoVEpXtUvrpJYQQQkgEiJhg/9NPPyE7OxsXXngh+vTpg2uvvRYAcN9996FPnz5K5PsjR45ozjt8+DAaN24MAGjcuDEKCgpw4sQJ5fixY8dQWFioKSOqAwAaNWok7FtCQgIqVKig+UdKHlpzdefnz99S9NwUh+y8eo9xsD+9IKzW0heXxt5OM3a7Qo198HDoSjZdGlVWtotrUY4QQgghpZ+ICfbjx4/HmjVrsHDhQixcuBBffPEFAGDKlClYuHAhunTpgo4dO2L16tXKOadPn8a2bdvQv39/AECvXr0QFxenKbNq1SrExcWhV69eAIB+/fph69atyMjI0JTp3Lmz0L++LFJaAzjJBtt2Gf/9P0Xni3zsReb5IYzl+4t3YsXOE/h69f6AY3pZ7mRWkaVKcdw/WZZd9rF3ry5CShKaBccI9oMQQgghpYuojpr25JNP4uOPP1Y08m+++SbatGmDiy66CABQpUoVjB49GlOmTIHX64XX68WUKVMwevRoVK7s04pcfPHFaN26Nd566y0AwPHjx/HJJ58I0+uRUoZGq+18Cp2SWBRbUuhjL5iWhyL8tqqdimveX45nf9qs7GtRMwVAoCn+d2uLUt8Vh9LP7Tb010NIWUE2/EEIIYQQEjwRjYrv57777sPy5cuV7RYtWuCLL77A8OHDcfToUQwYMACJiYmoVKkSZs2aBY8qV9bkyZPx0EMPoUuXLgCA7t27Y/LkycrxmJgYzJo1C6NHj0aPHj2QnZ2Np556CsOHDy/ei4xiSqs1qDdEU3ytYB+6EP/Own/x3dr9+OL281G5fGDQx+5NqmDW+oOafZd1rGNZb3HksbfbxuJtx2yVo8Y+eKQwhM97a952/LThEGbecT5Sy8W5Xj8pQquxL6UfX0IIIYQUO1Eh2E+ZMsXw2OjRow0D7AE+f/g333zTtP66deti9uzZwXaPlFC0pvjOJ9BNqiXjr90nAQBncgpM6zfb5+elX7cAAN5ZuANPXNzKV95CYLYjwhXHwkx+obuNuOVjn56dj00HT6Nbo8rwlJHVgnAYO7x6NivDf5fswv0DmrnfAFHQBr6MXD8IIYQQUrqIalN8UjyU1rllqBPo6ilFaRILBbb44nzz1g3lFXiFdYjkNVkpZ1xvcWjs35y/3dX63BLsh721BNf933J8vSYwLgFxToHXa12IhITmuxS5bhBCCCGklEHBnpRaQhZ4VcJnoa4uWZYVDXxxYHYlxSEc/G/FXlfrc0vrvDctCwAw++9D7lRYAghneAJqkMOP2nqI400IIYQQt6BgT0rc5DK/0IvjGbmW5dTXFaqQr9fY/7H9eNB1OYnW7++2WfeLI2VWXIy70qRaY+9G/5k2jLhJQaEXp1SZJ9zEq9HY87klhBBCiDtQsCclbnL5n49Xodvz87Dl8Gnb57gt94l87u22o3URKPoh0sTauTfFYT0d63H3U6F2h3/y+40h11e25PqyEUsgkgx9awk6PP07DpzKdr1u+tgTQgghJBxQsCclbnK5eNsxFHplzF5vbn6tiYpvs+5Pl+/Bwq1HfeeEaWA0priq/aJo54rG3uQKiuP2xbgcmE6d7u4zF8z8iyPOQLTATIHhZ8vhMwCA3/85HIbamceeEEIIIe4TFVHxSWQprWbMRppxIzYeSMf4s9rj3S9eHJSw6NT6wXYTJuWKQ6h1W5h0u75S+giTUojGq4cPLiGEEEJcghp7AkHA91KBWsi2c42H0nO055v5tRtI2k5N8dXM+vug+AAsgucVg3DgVhT7cNVX0txJohWOYvgprQuphBBCCIksFOxJiTVjtpINh7arXfTDxiXqqzNaDNiXloU/d5ywrtAAbfC8ol+igHx/7jiOfWlZFsHzgu6KbdzWsMe4LdiXzEeYRDnheKycBM8khBBCCLELBXtSYoUiq35XSIwrKmtjCq2XNY3OueDlBfh8pdgv3HnwPPOyS/89gQteXmBapjgsLtx263bdFN/d6qIautiXbLwO3n9CCCGEELtQsCfYcyIz0l0IC5rgeaoJ9JHTORg5fSUWbDmqKa8WNn9cfzCMk27nFe88lmFSW/ilg90nslytT3JdY08JiZQMZM13ic8tIYQQQtyBgj3B9+uMfbujGSeyoXr6PP77jVi49RhGffSXtj6VLvTez9fCK1CF5xWY55azM00PZi5/x6erDY+VxBgJLgfZL1Oaz3BGxS9L4xgNcLgJIYQQ4hYU7EmpRT1pVmvvj57JFZ8QYIofyKXT/jRv06FkZLf4mdwC19oMlSrl40Ouw/3geWUHUVpEUnIwsiQihBBCCAkFCvZEQ/cmVSLdBddQm6/bmUDrxaVD6dnKdpNq5fHKnK3YfOh0yP3S+Ni7IJK+NX9H0MK9LMt4Z+G/mLvpiO1z6lVOCqotNW5r7EtqAEhS9tC+/4QQQggh7kDBnmhISYyNdBdco1Blo64WoI2UxXq/7583HFa2/z2WiakLdli2acsUH+5q7HYczcDczUetCwpYvjMNL/26Bbd+ssr2OQNb1zA8Fh9r75Pivo+9/3+ZfsskqtEGz+SzSgghhBB3KD1SHHGF0jTPLBcXU/RDdV1GIqUromYIeexD4cDJ4ILbHTmd4/ic2y5ojAaVy2P9/lN4f/FOzTG7gorrpvhnBfpr3luO3EIvvruzOzxumwVECeH0sSfhpziCXRJCCCGk7EHBvoyxes9JFBQaB4ArrVNOO9cVCYHJrfEuzgB6cTEeXNyulsZVwY+ZXL985wnEx3rQqX4l18d6/f50ZOYVYuXuNADAodM5qFOxnLuNRAnhfEwpdIYfprsjhBBCSDigYF+GKCj04op3lpqWKU0TTaPgeUZm4G4EJbMjGKlLuGWKWxhGyd5JH4183dOz8nHt+8sBAP8+f5HrPvYAcCKjKCgiTZxJ1OJyjA1CCCGEEIA+9mWKAlvCX8mZaFrJhrJB9GlDU3wXhE078qQmKnboTQKwe2+Dw+iaRAskRr04nlkkdHtlOSxm8t+tPeB6ndGI2/EJiDHhWB9yO8YGIYQQQghAwZ7oKE0TTdlg2zB4Xjg7Y4Bb423mXmGGHRnRqIsi2dzO9cgycDIzz7qgQ6bM3e6oH4REAo0pfuS6QQghhJBSBgV7UiawZZrthsbetULOCK/GvqjuEec1ULadDJd6+Au9Mr5ctd+FnhFS8jCyJCKEEEIICQUK9mUIO5PIeVuCS5sWTs7k5GPij/9g9Z40R+dp00oVbRv50rviY29HY234I3jc8LF/dvYmLNp2LGC/uubYmKIxcmYSXlRLXkFw1gVOKIsC0w/rDuANldVCUJTBcStutJZEHHBCCCGEuAMF+zKE3UlkZm5BmHvijFd/24aPlu7GFe8sc3SeNkid6odhHnvHXQsZtyb2bmjsP1iyCzf/d6VpmViPWrC3X7e6e3kCt4GDpwIj7IdCaRaYjMZ97Bfr8PrcbViz92Txdog4glHxCSGEEBIOKNiXIexOIgsKo2u2+e+xjKDO05i8qgQ9I3nUjUm2raj4YTDFLfSGTwuu7mNsTNEnw4nGXrYQ7HPyC4PqGwnkVJb78QuIi1CaJ4QQQkgYoGBfhrA7ncwPo5AYDDEuRFDXmOIbVFdcWt5wtJIfxsUY9bjEqTX2QdYhMsV34x5r2qPsRKIUPpqEEEIICQcU7MsQRjnG9eQHGWE9XHgMw9ibC4OGUfENRFJXNPYO63Brkp9aLs6lmgJRX1OMR62xD66+3IJA7bzrgr2rtUUXbsSCIJFDG/ujND+phBBCCClOKNiXIUqqKX7QMp/qMtSLGoYae1dM8W2UCcPEvnbFRFfqsSIuVq2xD84Uf/CUPwKOu6+xj65nuKTAUQs/XkbFJ4QQQkgYoGBflrA5iRT5QEcSZ9HXi1Cbf0erKb5bLWblhc9HXT12g1rXVLadyOJWAozbWujSLC9ZvQ7U6Ec3R8/kKtul+TklhBBCSPFCwb4MYVdwjT5T/ODO0wqTdoLaBdeO00rsukQ4Yd7m8KUpVD83tVKLLANEAqaR5r24o9SXZk2oeoRFlgmlOSNAcRBua49jasGet4oQQgghLkHBvgxhNyNatJniq4XFYCfddvLYuyLXOyi7ek8axsxY40KrQLWUBFfqsUI9dqJxNBTsLQbGfWE0up5hJ6Rn5+Phr9dj6Y7jlmVF4/ra79vC0KuyQ3EK21yEIYQQQohbULAvQ9gViqPZFN/JpFvjy66pz6h8+CbZR0/nKNuVknyB7u6buQ4rd6e5Un84rAD8GFYtGMdYA8Heqn+ZuQUOe2VOTr5XGKSvJPDKnK34ctV+XP/BCsuyolHdeOA0dhwNLkVkaUOWZcfPVnGK2tTYE0IIIcQtKNiXIWynuxOkI4sk6qj4miB4FueptWF2BN9wWuKrF0t+WHcQALAvLduFFn3YtcbQM/aLdZZljBZFRNkKjMbZqn/9X1uMF37ebNkXuwx9awkGvr4YBVG2SGWHvWlZpsfVw260GJUR5EJJaQs6+NQP/6D1hDn4y8ECWnGOwdQFO4qtLUIIIYSUbijYlyFsR8UPVkoME2olsJOeaaPP2znBQeUqrj63rqodcSXq3WdyCvDLhkPBNWaAN4z3zOiaRAsrOfliQdqOsPTe4p1OumXJnhNZyMwtmVp7NXkFXkz/cxd2HD0DQGvBYnTbw2nBUZL4dPkeAMDrDtwTivPzVxhl31pCCCGElFwo2JchSqopvidIU3w12qj4wZmLG1E1OQEXt6tlu30AuPMzd3zr/YTVFF+1rR46I5eGXcczA/ZFSn4piT7M+h7/989dmDRrE/q/tlhQ1mghqeRdd7QQ7mcmPpZ/dgkhhBDiPpxhlCFKqim+WoB0IsBq08pZm/AHKwtJUlGdRlWEW1goLh97TfA8g4EU+TRHStAsDfLtmj0nDY8ZXZ9XBqYt2IFBry/Gqay8MPWs5OAkY6aTZ+Z4Ri4GvLYI/+fE2sRG/T9vOIS+ryzEPwfT7ddLCCGEkDINBfsyhF0T+/woioq//cgZoQbYDkam+MZ57EPHSCg4mZUfUr0XnFPV9HhYNeIGYyfysTcichr7kod+EUQ/zKJR159zKisfk+dsxdYjZ/C+yy4OpR0ngv1b87Zj+9EMPOcgPoSdRbgxn63BruOZuNOlrBmEEEIIKf3ERroDpPiYuXKvrXIF3ujQ2GflFWDA61rzY2ca2KLCds4z0irHeCRLX1gj834/l03707oDJvyx3Zf6rHZqIg6m5wQcLy6NuB1RXtSVSPl8lwaTdKP0jEDRWOsv87ZPVinb+RauNWmZRRr9aFrUcxOzMdTj5FkNxm3JSf1uZ4sghBBCSOmFGvsyxMxV+0yP16lYDoAvWFc0cEqg5dabtB/PyMVdn63BnxY5v+1E0zeabhvlZndSh1skxsUI94dTI27kRmC0mCEqHyn5ujSIqQHDrAkm6bvCUBZOjmfkKts5+SU/2GCoyAbbbqF+VzvWr2hRtjQ8wYQQQggpDijYlyHMhD9JAlrVrgAgerR2IrlRfQ2SBEyatQk/bTiEGwQ5v9VzYvUEuXxCkaGKOh2a0RzaKDe7pq+WJdyhXLyRYF9MPvaqm2J2zVl5BUjPLlqYOZGZa1I6fJQGuSjQFD8wKn4ol6l+vBml3ZmVh9raIZi661dOMi3P+0EIIYQQu9AUvwxhNWGNj/Gt80SLKb7IfFZ9DbIMHDplnAtefbVqwbdy+XhlO79QRmyM6IwiYizM7CVIivAVbtPvchHQ2B89UySUq0fCyMe+0CtjwGuLcTo7H0sfuxBbDp/B3f9bG74OmlASo+LrMTfFl8/+H1oLfkqrIOkkeJ7dIVi+8wTm/HPEUT/098nqvpXS20EIIYSQMEDBvgxhNomUAMTG+Ga/0WKKL5qM6y/BLICbWshWT5DVpvX5Xi/KIeZseXE9MTHFpY+3Rm+K7/f/D2ce+0XbjirbdtLdZeUV4sDZBZd9adn44I8IBm8rDYKRyePnvzwziw0nQn9haTBxCBWbQ/Deon8dV+3Usqa0LrQQQgghxH1oil+GsJpUxp3V2EeNKb5gn6xac5AkwGPzCVYLvmrTenVqP6OrjrXRSHGJ/gm6HNh+a4JwmuL7LTkA6yCBgDZYW2yM5Ch6vttEx5McGgEu9mofe1cusKgSu5kzSjN236XYGOd/PvU1W7XEhRZCCCGE2IUa+zKEuY+9pAj2BUFEeg4LQo190UUs3HoM6/adMjxdfblGE2T1Ioahxt7G/N0v8IZ7Hp6o87H3eAAUhlmwjxWb/xtRoBpTjxRZwb40YLqYYhAV3wnatJAUJLNtBhCMC8KSR/+eWo13OC1xCCGEEFK6oMa+DGEl/MWfnahapceKJOpLMBPq9WXV2+p0cep4Akb+2HY09lZ1uEWiTsgu0tiHr83mNZMdldfGaJAd+Te7TWmUU9XD6X+nQ3nuNLEodK/+4fQcLNh6tMQL/HYsTfyczCoKiGd23U6+C0X1OSvPqPiEEEIIsQsF+7KEpY+973HIixJTfFF/nUx0NRp7leT709+HisoYCP9q7Mzfi0t2rVBOa2TTuJpP6A6n4OXXuPvTIfoxuma1Obcsm8dBCDclMXie+lb+sf2YqSn+ibNR2c1uv9UIqN8pvSn++S/Ow6jpf+G3Tc6CxJVk7L5KsUFo7AOC51mVd9wCIYQQQsoqFOxLGZsPncZN/12Jv/efCjhm18c+WkzxRb0NdqJrdO3q/aH42PulL39147/fiFfmbHXQQ3tULBePiklxyu/bejUGEO489j70w2DUpNoU/44Zq/Hj+oNh6Zcd3F7v+O2fwxg1faUm93s4Wb7zhGm6u+w8n9l4KJdplBZSfWzxtmMhtBB5gl1aMnt+7KTB1PPvsQxdA46rIIQQQggRQsG+lHHjByuweNsxXDbtz4BjVnnso80UXzSpdiKoaaPiGwn24vJqYhxM4GUAe05k4tPlezB1wQ7XNemSBIw4r4Hy25/+rjjy2JulXVOTp3p+dh7LDEeXbOP2qNz+6Wos2HoMz8ze5HLNYrxyoFCqtkLIKzwr2Idw/9WntqmTKiwTLd+E4sBsoUNNTBCm+GM+WxN0XwghhBBCzKBgX8rwm+aKhHgzs2RfPvfoMsUX9TdYAUbvO6zsV2vsjYLn2TAlVwu9aiHIbU26BG1kfP+ag5vtBI6x77ddi/poStEVLheFw6o4DeHEK8um/uG3fLQKgPkChtUQqN+z6ikJwjLRkinDCern0MnChHo8zK46GI393rQsw7YIIYQQQkKBgn0ZwmqCX5TuLjq0cyL58Id19s267WjeZI0pvgsae1kbqMttIVeSgEs71EGn+hVxR6/Giv+6m9Gz9VUVaex1fTE4P1pcOYDwaTzDmRZuyY7jyrYs0tirmk7Pzg+5PU2cCcMyJU8AzcwrULbTMvOEZeZvOYJpOssa+xr70GNHlMBhJYQQQkiUwnR3ZQhrwd43UY0WwUwkTDz38+ag6rJnii8+1yqtlSQVabNlyMKo5W5Sr3ISvh3TAwCw6Kzvs5vteGUZMaqr8NdsN7J4SdTuOqW43hGvwBZfGHvCrSE3qCgaUhZ6vTI8DoRpfclCrxwgjPstHtrWSUWvZtUAaMfXbFzdEOwJIYQQQtyCGvsyhN3geXlRI9iHeL5qim6kYLVjim8lTEiS1vtcLQS5Ldjrc2yHwxTfKIBaoK+3mAIjvwcV13Wtj6nXd9QEAgwH4dKIFpe7SqEs24ttEEJ37GjsnaSLCwcnM/Nw3gvz8NQPG4M6/9iZXHR9bi7u+2Kt8LjatUKrvTceWL0pfjCLPdTYE0IIIcQtKNiXIayETL8GKlp8pEMW7FXnG12Ter9xVHwLwV7XplqT5/ZY/rLhsOa3fxHBTVPpgJRcBpK92tcfABpUSQJgT2NfLSUBQ9vVVhaTAtqKcsLVT329dgNImvlqf716H87kGJvs2/HzjrTC/rMVe3D0TC4+WbYH6Vn23A/UixEnMvNwIjMP3xu48qSpc9er9ptGxddZ8uw87jxQZLh97PelZeGnvw+56qpDCCGEkOiEgn0Zwmpq5xdIo2UOGOqk156PvXo7OB97jyQZCj6uj6U+9ZmisXfXFF+NYoqvK1ehnFbb7h+nAhuCvaT7X2nL5fEKl+AUrvUHfb2+4Hm6MsKgksZ1ns4pwOu/b7fVpqHVSoQFe7WQftsnq1yv/8Vftijb2u+G8Tn6qPhJ8TFudytkLnh5Ae763xp8t/ZApLtCCCGEkDBDwb4MYSWM+Cfv0aI1dbMboeSxtxLsJUkbFV9tuu+2pkzfE7/GftuRjMDCQWIYPE8nYVZI1Ar2/qN2TPH9VQUKre4SNgE8XAsGut9eWQ46B7uaj5bustWm0bu/YleaC71wh5W7w90X64CaQKAlTzDuCsX1qV2280TxNEQIIYSQiEHBnij4J6b63O7bjpyJSEC9UOe86km5Ufc1QqxBg1b5qtXze1kOf/A8NYln89gnxrn3Kgf62Pt+69c3/Kb3euyY4jeulgwAOKUzqxaN154TmcjILQjYb4dwjX74NPbaigu9AjN4oSm+OWbrS7KNxa09J7IMjpQ+7GvstTcmmAXR4lpCDfd3iBBCCCGRh4I9UVBSp6kmgZ8s24OBry/GvQZBp8JJqJNR9emv/77Nsg3/QoB+wm7lY+/xGJviF4Z5Ql01OT6k8+tVLhewT9YtghSZ4msvMjEuBjUrJCq//QtDVotAcx/ohUva1wYQOLb6e779yBn0nrwQPV+ab1qnEdFifWIXfW9lQfA8cVR86+vcf1IsnNvJDFESCfbea84yqUOfLSOaxy6a+0YIIYQQd6BgTxREEdbfW/QvAOBnXdC24iD0qPhFHDiVbdBGYFR8vRxvaYqPIsFelrXt2rBKD4miPPahna/GMCq+YBgqlCvKmFlkim9+45pWTyk6RzIXjvzp/PSafbuES57ZftQ91wc1orG3Y+Ft5zpX7DQyYS+dUl+wV2VXY++3likqG4TGvpiGnhp7QgghpPRDwZ4oiCKsRzbNVfgnoxpt5dn/9cLutiNnTOvQmOJDO35uT6j190PJZBCsdlJwWmDwPGd12/GxL2pfW3dugfZcfdR8p5Q0eUbf34Q4T2AcAsFF2bnOfANLCjvp7koTVpp89XGz9yowe4RxnXkFRu9E8Yx4tAREJYQQQkj4oGBPFMIRYT0UQu6GjfPVwe387ek19Bk55v7dvoWAonPspNlzi/BExdf+NgqeZ9SX/ALjvrxzQydt3brj7Sf9pskpHqpgX9JF1UpJ8fh85T7NvmBvdZ6RYK+pu2SPl5pvVu8X7r/j09Wm56lH4J2F/xqX042V0Tv477EMNHvyF0z4YaNpu+EkWr7phBBCCAkfFOyJgsisO5IK+1BlYrWmuW/zasIyao2c4mNvoBU3Ql1clK7MTfQ9iVGsLOwJZduOnMG3a/ZDlmUUFHqxNy3Q7zogl7pB24F985X4ffMRwzKVy2tjAmTlFQaU+XT5bmVb78fslJIkz8iyjK90wqhQOy8618YChlGGhpI0Rk6YNGuTcP9vm8TPZ6iLGkbfq2nzdwAAPl62R9BmSE3apjQt2BBCCCFETKx1EVKSkKTgJ4ui4HmRFOzdzGPfrEaKZRmjibll8DxVHnt9n8NtAqtedPDKgJUcPPD1xQCApPhYHEoXxx0I1Nj7dth9FtIy8wyPWS2SAFqz5fjYsrP2uPHAaYz/XqvVzbf7ANkoZmRxIYozEW0Ux3dow4F0tKtbUWBiLwvHzt6SCzQrYrVSE3FIZZFSXIQ71gchhBBCIk/ZmTWXEdTTz4ve+AObD522fW5RHnt1ffZn1OO/34jbPlnlmnbITSHDqCqNRv3sdp1K2kjxMRbSsi+PfUA1AIrDFL+oZSdtbTyQbhhMLdDH3t+WVV+s27UTs0Et2KtN8Y00zmZEqZwqJD07MEBgQaEX1VISlN+yLAvfCzvXaTT06nNnbzhko6bix8l3CAByCwItQdSI4g0cPOUTuPWLc0bpGwOtc8RtqWN29GxaVVuHaS/dg6b4hBBCSOmHgn0pZtOh0xg9w9yfVI0k0NjbULAqfLp8D37fdAT/HLS/mGCGm1Hx/YsNeuFQFDxPr1WOs8xjr/WvVwsGbpvA6oUzrcbeflteWTYU9ALq8fvYOxSugkWtpVZbS+h9xO2MbUmSZ0QWKvmFMpqrrE1O5xQIy9m5TqMy6v3r952yrqgE8OtG8ywecwXm+Fl5Z2Np6MbJTmwCwHh8RW9N46rlz57jOykjtwAb9qeHzWSewfMIIYSQ0g8F+1KGXiN6WqAFFDH5ynaqdHdqU3znwpxb2qHQ89gHmhi/t3inYRtGwfNiLTT2HgkqU3ydxj7MkqU6HoCT8ZJhosENkOvtmeK7lUFBXUucyhRfHzH/x/UHLesK1Z2jOBHdvgKvF4lxqs90CJdjJDSWpDHyk5FrHtAyN9/c9lwkrPstXvSjYRTR3m7wPM3Cn39D96pc/OYfGDZ1CX438P8PFfrYE0IIIaUfCvalDL1oZXc655EklY+983Y1KfIcaHYXbj2KR77+G9mCIGqhIgu2P1q6S1tGI9j7tvXp7mItNPaSJGmuWd1ucUXFd9qWLBvfJ8M89o57FzqaNnWX982aA5bnl3R5ZsbyvdBkXICRKb71ha7cbZDHvgSOkchtQYPFw2qW5lF/zDhVnfj8gK4IgmsqqUXP7t9zwhfE0s5iVTDQFJ8QQggp/TB4XinDidK0XFwMsvOLBGq//KrNY2+vLvW8MTPPXJumZuT0vwAAlcrH49EhLQzrDBX/xFYvzKqDSvldaZ1r7I3D4rs9n9b3Xx88zy4yZEPhxyjdndXDYOdRqauLXyCsx9AXXNsxO+2VJHnGTleN7rGd6/x5g9g8Pdi1p9M5+UhJiHXNUsMJVvEWgumRX4mvf86MNfbmv5W+SNqFGUAczySc0BSfEEIIKf1QY1/K0At+ZwxysM+6uydqVUzUnivQ2NudIKs1Qte+vxx/GWkHDdh6ONAv382o+EYTaHW/n5ntS491Kksb1d0qKr4UYIpfVOf+k+LI826hXlRwFFzOWK43Dp5nUaWVfNehXkXUqJBoXsgE/T20I0+WJDNzezEDjMzpQ2jX4OycfK0VzafLdivb/xxMR+dnfsfYL9aF0LJ99PfaSgNttdgguubCs6t8+qqNskfo6zDqkubzYRGvQoY4sF+oLN95wvU6CSGEEBJdULAvbejmi0bm2W3rpgZIA34hccOBdOw4muGrzqY2Tt/Ma79ts3WeH5Fpbeh57APRy+jHMwJTs/17LFPz28oU36OKir/hQHpYRUn97VBfjxN/fhlQ7nHAsQCrA3s+9la0q5tqq5yRW0NgOWtKh8Ze1mzp788f24+F5ENtdOrxjFzN7/E//KNs/2/FXuQXymEzHbfC6tsQzKO66dAZAIH3Icemxt6/2PDrxkNYsOWosl/kY+/ftWjbMe0CigwcDkM6vPqVk1yvkxBCCCHRRUQF+x9++AFDhgxBv3790LNnT3Tq1Amff/65pszRo0dx8803o0ePHujYsSOuvvpqHD16VFOmYsWK6NOnj+bfjz/+qCmzadMm9OnTB7169cK5556Lb7/9NuzXFwmClb1kaIXE/q8tAmA/Kr5eg+bUp1NkWRBywCeB/7x+oWLjwXTLaqxyr0uShKyzMQI2Hjitm/CH28deEgY9tMLrlbHl8BnhMb3C0C2NfVDPpizcLJ3YMLP3ZV3QMuLDleFo1nRRJNKm3VbPusVanPDaPl+59+wxe98y/V4ZQFpmHkbPWINRH/2lLKqK3gv1u/fVqn2WfQuVCHhLEEIIIaSYiaiP/TvvvIPrr78eN910EwBg1qxZuPTSS9G6dWu0a9cOXq8Xw4YNQ8uWLbFkyRJIkoQ777wTV1xxBf744w+lng4dOmDhwoWG7Zw5cwYDBw7ESy+9hBtuuAHbtm1D586dUbduXXTt2jXcl1mshDKBixGcbDcQnpH2yi4iy4Jg5rfpWfm4+r1luKRDbV1/xOWTE6xfATs+9r2aVcNXq/fjeEYuPvijKPK+00n68E518K2NoHD69r2yrIkXYIU+wryaQVMWY834AahcPh6AKniexfNh9awE44ttZkqvD3IoPL8ErQYYXes8lfbXqEwo16kWZFvWqqBshzvwoxMCgoJameJbPItmpwcI7DYH1yvLGssjrywjBpIuHWZgXev2aRcXw+E+UlypKgkhhBASOSKqsX/uuedw/fXXK7/79OkDWZaxc6dPMFq5ciVWrlyJsWPHKkLB2LFjsWTJEo1gb8X06dPh9XqVtpo1a4YhQ4bgpZdecvFqooNQJnChBMEK1Ng7O19kRh6MsPLhkp3YeuQMJs/ZqouKL67M75e+Yb+x5t7Kx75Xs6qonpKg/P7iryINnJNLOKd6MjrVr+TgDB8ejz82gv3Wth8Va+v9vLNwh+qXNuBXsNh9vIzT8Mm2ypVUbN0+2f3FCnV1VZPjlW2z56k4x/7YmVys3nNSs8/o+7IvLQsbD6SH1j/9IqXBGlhg8Dxrn3sr9yCjrAehUtreFUIIIYQEElHBvnPnzoiN9WlM8/Pz8corr6BVq1bo378/AGDvXp9pZI0aNZRzatWqBQBYvHix7XbmzZuHzp07awTXLl26YN68eSFfQ7ThKOWZ7rdIcLM7IQzVFF+osQ9ihptXWHSOKHie/npkAHtOZGLY1CWGdcbGGL8myQmxqJ6SqAjXoRLMBNzftJN7b1U0XzWO/rKiRSP1golV3+1o2APqD9EUv2QFz7NRBuJrCkkYVJ2rfm/NnpHilBO7PDcXv+nyuxs96xe8vABD31piGbRSdLY/BoR+fI1N8QMFefW4+M/TauwD69En1Sg5TywhhBBCoomoCJ531113oVq1apg7dy7mzJmD5ORkAEDDhg0BFAn4AHDggM9Mef/+/cq+w4cP45prrkGvXr3Qv39/vPvuu/Cq1Cw7d+7ULA4AQM2aNZGeno60tMDo7bm5uTh9+rTmX0mhmkpz7BSRcBps8DzHGnuXTPH12i99XQGCvQz8n8p0XkSciSl++YSYgHb19TshGIsLvwuFk8WUlbvsZy0oSndnXs4q37fdK9MEETe9pNJmim+jjIHGPpQ85Zr3RLMYFh0aexFW1zt5ztag6wx0KxKXF5UTZb7U7LPRj5BjixBCCCGkTBIVgv20adNw/Phx9OnTBz169MChQ4cA+LTq5513Hp5//nnk5uaioKAAzz33HOLi4lBYWBRJuGnTpnj++eexePFivPfee3jppZfwyCOPKMezsrKQkKAVeP2/s7KyAvrzwgsvIDU1VflXr169cFx2WPALmmb0b1lduF+osbfZrlEkdbsUemXIsowTqkjcwcxvjc24xfu9sowZy/eKD54lxiQSl18bZ7QA4kRjrE6bBwAjuzcMKHPDeQ0C+6CY4ttuypKM3KJghv5rsHoWjILx+QnGqkHjTqG7Pnvp7koOttLdGVyRk4wIge2Kt0OpM9yEHlfT2Ooh2HghRkH3NIuNZ/fFxxZ9UzRZIMKksQ/FzYoQQgghJYOoEOwBIDY2Fs888wy8Xi9ee+01AL7JyM8//4x69eqhT58+GDhwIAYPHowGDRqgUqUiX+TZs2ejSZMmAIAmTZpg3LhxeP3115Gd7TPHTEpKQm6uNnWT/3dSUmAaoMceewzp6enKv3379gWUiVbMzJ0bVvFd66gejYTHhcHRbJvi6387m556ZRkPfrkenZ+di0XbjgEITnOlnyQXbcsBxwF7eebjTARS/xGjcXdyCRUS47R1C6q8rkvgIpO/bTeDnX29ej/Ss3yBwIzcGABnFgbBiBbqZ0Av1Opvi6h/pU37aST4bTxgnd3BrE4/6vf2p78PBV1nuAnFQsG4Tt//+poNTfEFcUXU74PQFP/s/0Pb1lL2aTX6YfKxd79KQgghhEQZERXs8/K0OcQ9Hg+aNWuGTZs2KfsqVaqEadOmYdmyZZg/fz5uvPFGHD58GG3btjWst0mTJigsLMSePXsAAI0bN8aRI1ofzcOHDyM1NRWVK1cOOD8hIQEVKlTQ/CspmAn2fl9xfxH9xFR/7qu/bbXUwvoJ8LF3EKEd8Aml3671uVlMm7/jbJ3O6gD02jHxtpq5m4+ID6jrNBPsz46ZoSm+Ze3A6N5N0L5uKiZf1V4zARcJzaLUex6D+xkqK3adQE5+IaYt2GHYHyc40Rr62910yNgNRt2fp4a2EvauJIn1tsy0IX6Wx36xzpV2V6hcNN6avyOw8FkiHWU91DUs0en+90cksAvrkPW/ZY2QrsSmENjiq78pga+F+08tFfaEEEJI6Seign2nTp0C9h06dAi1axelKtOnsVu7di0KCwtx0UUXAfAFxtPnrD9w4AAkSULdunUBAP369cOaNWs0E7ZVq1YpQfpKE2Y512WBBslP0+rJAcLpW/N32NYCuxk8T/F1DWaCK9COqesKJTidWXPBBIbzM7hNTfxwd080qlpe0z+hhlxoVeHb57Y4kJFbgKnzdyiLO0KNuENXA3vlJLy/eCcmz9mKKXO3qxsz5LzGVYRj47b2M5wCkq3geWHRVAdhGRPlPvbBoJjiB+w3Cp5n8VsO/Ob43xftN0U7mKXMyIQQQgghxUREBftNmzbhp59+Un7PmDEDW7duxc0336zsu/vuu5UI+Dk5OXjssccwYcIERdO+b98+TJ48WfGVT0tLwxtvvIGbbrpJCcI3atQoSJKEL774AgCwfft2/PLLL3j44YeL5TqLEzPtsmJSLTjWoV7FkIRT/WR0y+EzQUdp9xrNsG1gFcQumCu042NvhB1BzEhLLwF467qOjs53m/X7TxW1E2JDdl3sP1q6G6/9vs26Pt1tEVX/60b75uRer4yr31uGS6YuQUGh2OQklHfEGhs+9rJ5uQqJsc5bDWb9zPkpruINg8reaLHA2BTfvJy/i+rnPjvPFxsmxkBjHz4f+zBUSgghhJCowvks0EXeeOMNPPfcc3jhhRfg9XohSRJ+/PFH9OzZUykzaNAgjBo1CnXq1IHX68W1116Lu+++Wznu18b37dsXiYmJyMjIwMUXX4zx48crZVJSUjBnzhyMGTMGb7/9NrKzs/HRRx+ha9euxXq9xYGZ8OSfMPqFf72GM5TJn2jyu27fKXRuUElQGjiVlYePl+5Rfms19tr+OkHrY6/2zwb+2H4MB0/lOK7TJNudKxp7s1NrV0y0XU84NH3qOkM2xQ/xfP3laRZBJPE4/t8fu/DExa2U30dO52DTodPo06xawPN/KjtfyRZwKD0H9SoHxt/wSEBhwF53sHv/zMpJkgSP5NRUveSpiN0MFOnHX2WAwG7TrUjfJ1FcjwVbffFD1N8L/WMbDmuESLtOEEIIIST8RFSwv+eee3DPPfeYlnn11Vfx6quvGh6vV68e3nzzTcu2WrdujUWLFjnuY0kjxkRKLJpoiglFOBVNtM201cOmLsG+tKLAdWrB3n+e0wmu3sdVzbJ/T+Dr1fvFBy0wGxf/MSOlvp1L0Ey6LUzxhef7fexdFtAkSXsPQg2eF0RQfA0BY6kbK19fzMfg/BfmwSsD79zQCUNUAcwAIN9AS6/Gd7/DIwjb8rG3KCRJPm2wt9B+H4PLPhFpH/vQ7oHoXSmq055bUWAee23gO5HG3o9hsE2EZ4GOGntCCCGk9BM1UfGJO5iZ4uuDOVkFz3OCyDTWbH6qFuoBbWotf1VOtXKyrJ1E56ryqh84ZR393gizcbGKin8yKw//+egv0/olsVwfIDxNGNYKYvz307QZx0iQ3NUeuixd6GvLsyGY+5+pP/89HnAsT/W86K975l97ccenq1AQDlXxWWz52NuImm4WZ0NcZ8kjZMFeGD1PfMyoKVE5tbCvfF8Fz736Hmmi5svuvcelLSMEIYQQQswJWrDftWsXli5dCgA4efKkax0ioWGqsdcFkLujty9F4KDWNQCErlENaM/BvFIUPM+pH60+7/au45mOzjdiYKsahsfKJ/iMXozGbtKsTZi35ajttiQTE90rO9c1OMf3f9hN8UXB6RyIhSFr7E3aMrMc2JeWFSDkxApMLAo0ViPaY498swFz/jniakpBPXbG0itbjYP42sxIy8yzLhRliJ71UAXZU9n5SM/KDxhd/XdFaS/gd3Aae+1rJbtmebM3LUvZDudzSwghhJDowLFgf/LkSQwePBhNmjTBiBEjAABPPPEE+vXrh+PHA7VgpHgxm9P7fUX9k8pru9TD3Ad6Y9r1vuwEoZjXijRowU60izT2DgV7r4xXfrMOuuaUFrWM0x3GxYjjFTjB0CJAt9uoDf/ecJjiq2/B4m3HQqsvVB973eXZtTC54OUF+HDJLstzf95QFGgvHH7OVrgRFd/vY2+XzNwCPPn9RvsnKO04PsVVxN8b++eLiqZl5qH9078FuGQYPguCFJ/qsqI89n5MY6G49Oip3ze1NQohhBBCSieOBfv7778fjRo1wl9//aWkpXv77bcxevRo3Hvvva53kDjDjrDjLyFJEppWT1by21sJBJPnbMGavWLrDKGPvWVPxPiFF72Wyap/dnykg8GOoORW8DyjCPn6Y6LzwyGLuingiu5PzQr2gwPqsUoNqOa5nzdrfsfGBJ4wbUFR3vZo1W9a+WBLgPI+20Gt1XVCpIOxiTTQwcTkEHFSZ8FgmMde0L6s+w2Ivx+aqPiaPhl0NgjUvWlQpbx7FRNCCCEkKnEs2O/atQvvvPMOOnfujLi4OGX/VVddhSNHjrjaOeIcO3nsjYQgK+F02oJ/MfztpQH7vV5ZOKkOVij0n6afUFv1r8BBwDAn2BFi7GpJL2xRPbB+AwFVf7lG1x9OIctqRJ20rdeaA8BV54rdC0TohTnJYFuEHW2/xow6AqbLdoPnmZXzB8+zi34ckhPsxVONtMZeaIrv8HxDRXxAWQNTfL2Pva5skYu9QGOvSXenPe6WcJ+v+R5G61IVIYQQQtzCsWCfnV0UhEw7iZEp2EcB+inkxe2KIn8X+XyKZ+XBaJ2/Xr0f7Z/+DSt2pgUeDHIu6fdp1S8MWPUv325eKodINt4SuwLuuIHNTc81u0RDi/0QNfZJ8TGGx1bvcS9+RnZ+aIniLnh5AabMLXK1CMX9IVYg/Kp9qQe8vhj3fr426PqDQS9Aii/POnie6NqM0Bc1M/Xv2bSq7XrDTagLiXrtuprAdHdGPvba/YFR8Y0XUs1ukVtWMgWq7yHj6BFCCCGlH8eCfcOGDfHEE08gLS1NmVhv27YNI0eORLt27VzvIAkN9STfygc7GDlp3FfrcSanAI9/t8H5yQZ4DQR7K9k5fBp74PKOddCwSmBec6WMzbFLiAt85Wy62FuWC8bHvmWtCuh1TjVxvTYuKlS/fqcCx5S524X7nT67Iq22XoD7cf1B0zrCHXVcdEnWTUqOFuj099jMUMHIZSQSiNNrOjlfNtbE22hL1J5XZ02hZCERjJb6Hp3Oyde07Zpgr/oeUq4nhBBCSj+OBfu33noLixYtQrVq1bB48WLExcWhZcuW2LlzJ954441w9JE4INCMtGjbSmPvNE2W0744PdGpj324Ij97JAmvX9MBC8b1MS5jc+xEpYz86iUJqJBY5O5ilPGgKH2hdfvxOv/rQ+nZthcWoo1QzMFFWu3G1Zz5Ibst1wesYwmzEFhExZfE8QPMymvrN647mrS+doPnPTVUnCLSzNVCL/Ab57EPbF9d1l+PVVT8b9ccULYTYj2O03waoc3yEEU3jxBCCCFhwZ5DpYoaNWpgyZIlWLBgAf755x8AQJs2bdCnTx+3+0aCwGz+ZpJWGUBoAeCc9sWMQllGbkEhxn6xTrPfytw9fMHzrCPf210TEdVh6GMPCefUSMHjF7VA1eQEy6Bodobb4wGgsog/lZVvWNYOIUe6D0GXKDnystciWoi5qnO9gCB7ZnhlGZ4Qr3/ynC3YfSILU6/rGDAWhhp7i+B5ofjYmwmVaqE10j72IsFcJIDHGSxyFJrEKrCbx17Uvn4h9XB6Dl74ZUtAWaN71LxmCrYcPm2vQQsKVN9DivWEEEJI6cexYO+nb9++6Nu3r2bfzp070bhx45A7RYLHaAJ34FQ2Tmb5oj0b+9jba2NfWhbqVTY2S/cTrEmpV5bx47pAM2ir/hWESWNvR4ixuyhiFP7OrN3bezUxr1Pxsbe+fl+Oc+0CSPgsHcwFxVCQZRk7jp4J+nyRxt6psOrGpU1b8C8AYFT3hgKNvahNa3caOz72uQWF2JeWHSj4mlS/81gmMnMLUD4hNqT4Bm7glYHsvEIcSs9G42rJAMRdjzHI/6n3h1ejz1t/4FS2sFygKb42iKhXlvHhkp3Cc81u0RPfOU8/KEIdPI8Ke0IIIaT049gU34xbb73VzepIEIiEu+MZuejx4nxFgDMOwmZvsm5XgA52LinL4jYsg+eFSWNv1qxso4yT+tX3wG6VimBvo6xIU5hrkOM6VNkt1kCoUlMpKT6oumf+tQ/r96crv13xsXco/biZCjCvwGvPFN9E0+zHSJhVc+37y9H/tUX4fZM24KnZNR0+nYMBry2yrLs48MoyBr+xGBe+ugirdqcp+/QYLXIUemXDRRL9QtdHS3cHpMADxIss7y4qEuRnLN+DE4LzAGPXHTcXwtTB89x8VgkhhBASnTgW7D0eD2JiYoT/Fi2Kjkkf0bLpoNa000hbZFdjb9dfU5ZlTFuwA1+t2mevYuW8QF9wAJaS7pmcAkft2MWONt62xl5QLFTdp98k3c5tEQk6eUaCva5n/VsGpuoz0yDb8fW+8bwGGNKmJh4eHJgtwIx3F/2r+e10DMWCvbM6XPex1/02MsU3e/8kSDDz2NhzIhMAsHbvKQC+rBZGfbihW/2A8w+m5+DXjYciHn/BK8vYcyILADD770MAAFnwGBuZvBeaaOxFZv7bj2YEFhRo7GepAi5+smyPoauKUbwM0b3NyC3A+O83YsGWo+IOGxCuYKKEEEIIiU4cC/bt27fH/PnzlX+///47pk+fjqFDh+K9994LRx+JA+zldzYyxXd3ur7zWCYmz9mKh77+21HwJq8sC6PHW/XvcHqO4z7awc6o2DfFF5mAG2jpHS8WWI+xSFOoTomopkCXPrBXM3H0fCPs+HonxsXgnRs746rO9RzVrV+MsDP+aoFNmO7OoWTvpmAvqipYU/z0bOO4Cde+v1zzWz9u6vc05WzgxrqVymnKjJ6xxrQPxYEorZxobIwWl8zund4UX92Gpg4bdRovohpp7AMr+XTZHny6fA9GffSXuDID1BZMVNgTQgghpR/Hgv0bb7yB3r17K/8uvPBC3HTTTfjmm28wZ86ccPSROCAgtzLs5sd2onW2V049sXSSw9wry0iIDcytbiUn2o1M7xR7Gnt7dUkS8H83navdpzsu2m9a59n/7UzeRf28potYqA4MXihq27iX5ePth/Bweuv07gNJ8YHPi5581UKFyFxdtPhktiDlpnmzSBMvGlsrU3wJQOtaqYbHD+kWv/SPtnptw/8tES7QRDp4nmqs/AsyonUZI3eQlbvSjOsWBeYT7AuMnh9Yl9HiltG3SvRI7TousBawgSYqPsPnEUIIIaUex4J9r169hPtjY2Px77//Co+R4sOecGcQrM3ViAtAfGxRhbn59v3fvbIv7ZMeKwHbLIVVKNhZx3ASHb5GhQTNb49GY6/aDiHSvhGi5yPOItp+sLw3orPm93iD1GMAULm8M197fQyGpATrRQS1Rl50yaLHx+yRcvNp++KvvQGLFUa31ewdlyQJKYn2F1REdR09naM5JjIbd/I+hwP1vTx2JheAeBHGaMGodsVE28HzAPFzcFC3SCKK8WH0bhp5qXhlYMR5DTT71N+HQ+niQH4iqLEnhBBCyhaOo+I//fTTAfvOnDmD5cuXo3r1QB9cUrzYMxkVz/LsauyDyYnsRLspy7JmUcCPlQAbvqj4xu12rFfJV8ambCxJgYsAxnnknalFwz13dzq87etVxMRhrTBx1iYAQMtaKYZlnUZZ1z9P5eJsaOwL1YJ94A0TPaOFXtlQ6+qmxn7234fw126tFln0Ptppsl3dVHyl851Xsy8ty/T8J7/fiPdvOrcoD7vg+vV9LW7U4/Db2QCAoudT/1w9NKg5Js/ZCq/XWIstXuAJ3PnTWd9+P58u2xNQxtAU31BjL6NSUpxmn/oahr65BKvHDxBXqqOAUfEJIYSQMoVjwf71119Hhw4dlN+SJCElJQUXXngh7r33Xjf7RoJAOIHT7TPyJbZrDm1XwNPndLaLVzYw+7boX6G3+LWI4wY1A+DMjSHPJHq/UU57M06f9al+Zc5WzLzjfNOyRrfhs1u74bFvN2CvidAXTFq8k1lF/t5G0feDQtcVO/78lhp7m7nRlS64/LgdOZ2r+S0MnmfDqPr6bg2QlVcozJ8OmPvgA0WCspnG3ijgopu8t+hfpS96hAuYNjT2/vfULHjexgPpAfvsLOJsOhSYf97QOsqgDhmCIIqqwkZR9kWoY2TQFJ8QQggp/TgW7AcPHozPP/88HH0hLiDSzusndUaabbc19upSTrSbaZl5BunuzM8LU7Y7U5LO+pHb0RgDvgm93mTXyK/erg7bP9lfYeI3bEWPplVx43n18fzPYmEQCF1DnZNnP86CFcH0pUA17iJrCNFrUeiVDZ/3sAtLouB5VlHxJd8ixx29m+CbNfux7Uigf7adofv3WAa2HjkDQKxdLg4x0WhhAhDfK1Gf9N80/4KOVza+e39sPx5YdxAXnBjnMf5mGQXP82oXHMrFxQQdzkDjY0+5nhBCCCn1OHauNRPqV6xYEVJniAvoJnCSJAVM6ow0r3Y1xCIfVBFqDahTQezhr/8O2Gdlmh4Jjb0fOxpjwDfGem2nJip+kNo5u4QywQ9GY6+2TsgpcFOwL9q+uK04qr8eq2BiomfUK8uG1iZh8vxQED1RZgIpAOw/WeSD7cSHXE+/VxcpAq4og0Aw7jhuYnSv9Og9LvyCvtdkwUaEnWdfP0yNqyYbmtwbfS28svbZTE6MDTpbicYUP6gaCCGEEFKSsKWx37t3r63K7r//fixdujSkDpHQ0E/7RUHoQtXY2xXw1BNt9SlGQe5Sy8UpZsIHTgUGibKSncPlY69m9j09MfStJUGfL0EK1NgblN0sMO2NJHYXdADg/bOB89RB1rLz3Ft4UT/ncUaRyHRY+RwLfau9xs+7k8Wqw+k5eO7nzRjSpiYusrkQcTqnIGCfusU7+zTBOwuNA5Ya9U7db5H5uB0iLSiKhHKhj73u7VIEe4cXYOde6+vML/Qam+IbBUaEVmMvy7LtBVc9mu9MpG8YIYQQQsKOLcG+YcOGlsGtfBOQCOdAIgECi2g+qs9L7ceuYG9XMW4kzP++Wew3W69yOaQfMPb/tUpnF4xG2Slt6qTiik518c0a4+BkZkgS0KyGNoicVmgoukYnKQLtY2LGbWERYTfrQKtaFTCwdU0AQO/m1fDfP3cBADrWr2ivizYI5lZrfI5tBkgrlGVDoc6J0vqdhTswa/1BzFp/ELtfvNj+iaI2z7Zbs0KiaVmjfjvNHrFB4HOufowycwuQFB9TrN9/0bsuTFeoe979n5BCC8sHPcE8bz7BXnzMKBWkPp2hbBBvxA5Md0cIIYSULWyZ4nfr1g07d+60/Ne1a9dw95dYEOBjj8AgUVWTEyDCbvA826b4Go190fZJAxNzqwUD6+B5xTN5DcUMWQJQr3ISvrj9PGWf+rrV1xhtfrF2h1d9Db3OqYp3buiE6aO6oGWtCu51RtUXuwJloUbQCUQk8BZ6zQR7+zdo30n7acrMKYqZIUnA/Ad7mxUV4sZroq6i9YQ5eOSbQNeZcCL0sbexz+8yI5sEzxO3F1i4R9MqpufIMAmeJyEg+r2/HW3Q0eAXzAuY7o4QQggpU9jS2L/00kto0KCBrXIksgQIG7KZjlZLMAKS3b6oTzHSvFuZu1pZFDgxFTdj6vUdcff/1hoeDymI3NlLaF+3orLLrX7bwW0fe9EtUbchSRKG2DQ9d4L6HtgVe9Tp7sR1itvJLzAQ7G2266/HDfTVNK6W7LgONxbA9N+ZL1ftx8tXtg+5Xrvox/O3fw4Lg96pGdm9yPLM64WjGyi6f3Gi1AoqZNn4m1rolTUZI9TnqLXrXjnwHftm9X5c0bmuZZ/z6WNPCCGElClsaex79eplq7L/+7//C6kzJHQCNfbuB7qyW5/GFF91jih9lq9e8/qsBLhCC8HNLp0bVDI9HkorfnP3WJVfeKHGRDy85rOh1FivclLAPrtBA90mGEFZo7G3GXyt0Csj1yDon5M+iDXMzq/B9z77tq1G3qh2N74HkRYU9Zdw+6er8enywDzyFVVa8XNqJGvT3Tm4CrsWAprjkA2toAoMvlVenS2+V5YDFjQf/Go99pzING8c1s87IYQQQkoXjtPdAcCMGTPwyy+/4PDhw5oJw7p169zqFwmSQB9798VD+xr7om21mbM+UrVSJkSN/au/b7PVr1AJSWF/9hLUixuaGFeyeDvSPDy4OYa1D9S8iyKmh9vVOr/QqxG0/j0WmNJNeJ4mr3cgRpHWc/KNhDBbzfraE+Zdt3+++hzlPIuBNvSxd8MUP8LPpp1FlWcua4NO9YsW6WQZiI3xb2tN3htXLY+dx42FZbsLQdpzgG/XHBAey8gNDIzoq1P3bBr42K/bdwoNqpQ3bV/dvyj6lBBCCCEkTDhOd/f888/jlVdeQYMGDbBz50707t0b559/Pk6dOoX+/fuHo4/EJgd2/oNGB2ahh2eDsk+t4XOL4Hzsi/YbCeiWtRaTctgqiNzo3k0AAFfZMIcNrNuHxyOhbqVySEmIRcOqRZpw9RiEaja/dMfxAAEiWM3drT0bI8EvFamwCmgYDvTZAtbvFwR3E6BZkDIwu9ezZu8pQ429k7F06x1Ut2mpsTdo0w3Xj0gHY7NzDSPOa6AxhZdRZBqvX5wc0ramaV1mQrxhhHsZOHw6R3js6Jlc4f71+07pXJhk4Tv27E+bTXrrP1fbF0IIIYSUbhxr7H/44QcsW7YM5cqVw7JlyzBhwgQAwJNPPomRI0e63T/igAPr5+P1+HewsLA9/vS2BRCeCZ39qPiycNvIfNsqWnew+ZydYtVMq9oVsOnpQSgXFyjoOmHhuD4olGWNwCyby56W+LNTvL94J176dQs6N6iEb+7sbutcs+s2umcijX24CVbjrE7/Jc5jH3jOvZ+vxay7ewrrCzX4WlD3F/aFaqNybvj72/0GhIvgrB1kxVJGrxm3WswrFFyvvw8eSXK8WJJXIB7ATYdOo3bFomwHIh97QBx4L6B/qiukXE8IIYSUfhxr7JOTk1GunC9dWkFBkTawXLlyOHJEnMaMFA+e2HgAQCyK7suP6w9i9IzVrrZjdxKr1orZ8bG3NsW31WyxkBQfGxAY66d7xQKgGvU5sTGeAC24xnw2BD/yr1bvAwCs3nNSczzYCb7R2BfXYosbWLmQGI23kcb+dI5xasbAuu23Z1WP4mNvMfRG1TtNdxeNTJ6z1fE5slzkBuSLPq+yfrAYS/+i0I6jZ3DnjNXYfOi0IjgbLXqZPR95hcapLP9WWaB4ZVm46HBV53rmHQbcM/8hhBBCSInAsWCfmZmJzZt9ZoBVqlTB66+/jgMHDuCTTz7Bvn37XO8gsY8U4xPs46Rw5D8vwq7GTyPYqxRURubbVrUWm8YeQMMqgYHirGhdO9UypVuwAc/s4l90iTMKZBAkRtG91UEAQ+W6rvVdq0uEOmCZ6BE20kIbaVenzt9hu23XNPYaU3zf2NcXBDX0lTXqS+A+p9YnkQrGVi0lMFVn+Xh7fZdVgei8utgjVk9xbr7vm3rd/63ALxsP44p3lirPi9FC5ZkcsR89AAxqbWz6r5fHRZ9LO99g+tgTQgghZQvHs/+bbroJo0aNwr59+/DQQw/hySefRP369XHLLbfg0UcfDUcfiU08sT7zzHgYTyhdweYs0UhjbySgW01Wg83nHAzBtvW/W7tZ1Gt+fqgCkyJsGC2euDzDd3Ox5ZYeDYM67/nL29oqV2CVx95gcAoMNNxGftIixBp726crPPLt3wF9t+tq4UdkuWBmDRMfG/hnIlKC4qDWNYI+Nz42pkiw92rHv1y8uVda7tnFnWNn73lWXqGisQ/GkuiCc6oZHtNnxhC9YkaLTdp6xNuEEEIIKZ04FuzHjBmD5cuXo169eujRowc2b96Mr776Cn///Tduu+22cPSR2MQT59NmxYZZsLersS8wMsU3iopvMVctNrE+hIYqlY+3qNq88kpJRecHMxn/bdNhfLFyr6uadDNECwjByvqxFnnBjUgQCJ4i5vxzWNkWauxLQLC5fWnZAab4Ii02YPyeOl08Emnz7VTx68ZD+Gb1fkdtmXF5xzpC7bioK1/cfp6y/dCg5jivcWUM71RHk+7OjyQBN53fwLRt0TOg+Ni77COkTRMqHv88kdO/ST2RDnZICCGEkPDjeCZ95513an7Xr18fw4cPR6tWrVzrFAkOj98UH+E1xbcrF2g19kX7DaPiW/nYByH3Gc257QSfCgsWMoDalD+YyfjYL9bh0W83YG9alvC4fox7NTPWHNrBzeB5wdZkdyHha5WQKRpbo/E2yjnuBGEe9KCFLd95Vpd9fpMqtvtiRjBBImVZxugZa/DgV+txxCAyvFMeGdxCaD2hX8CoUSEB5zUuuva7+jbFF7efj8S4GGVRUdblsS+fEIunhhr/DTOzuDCyjgkW9Tvl1aXl82NLY68Onke5nhBCCCn1OBaVPv30UwwdOhSff/45cnLcmbARd4iJ8wv24dXY250jqrVcr6iCXRlGxbeoOBizbyOTerO6JEu9evBYXYJa+xrKZPxUljhwl77Kl66wZ8ZuhJtCjd37qy/lljuA0XgX2NCOWiHSnufkm9f7xEUthfvnbj5qq80nLmqJJy8OrEOkfTZzPRHdY9E+jQm5qon0bPtBBs2QJLEbgf5yzN5eTbo7v+XD2WNOH2UleJ7LLkLq51mWxd/F9xbvtKyHpviEEEJI2cKxYD9o0CC8++672Lt3L/r27YtbbrkFCxcuDEPXiFM8Lgj2jw1pYVnGrimvWiBatvOEsh1sVPxgps8SgDoVywXuNxPsw2jF7qRqN0zAA9BVWSs1cGycoBbwHhrUHJIEPH1pm6Dqsjvu+lEJ5n6JTfHF451vsOLkxKR974lAC4q5m8yziDSuVt70uNV1V0lOwK0XNA7Yb9Rvo0Uau+OrFuDDEZBdgr0ghGYCuijdnf9bYLZIZbag4LYp/mGdhYPRd8Dq+dP66hNCCCGktONYsP/mm29Qt25dPPLII1i2bBnuuusufPfdd2jZsiXGjx8fjj4Sm8TEhh4V/47eTTCkjXHEZsD+JNHIhNlIqLbS2CcEYRLskSQ8MKCZYL/jqlzBSVC+ljXNI+wHgxs5zNU0rZasbN/Vtym2PTsEnepXCqquYBdU/GNa3cDXXIQ4eJ64rBsa+xOZecr2mbNp0ApMgkrUqJBgKTCqNdNt6vielbqVrBdqREKqBODLO84XlhdZRIieI0mjadYGgHMFySCQoV5jb/IgadPdKdVanie6Xv8+pxr7lETzQH0B7Rg8mJbpG9XbVNkTQgghpR5nMwwAa9euRceOHQEAe/fuxZw5czBnzhxs374dixcvdr2DxD6xZzX2taS0kOqxEijsThLX7z8l3G+kGcvMFVsaPDakBf49loFzG1bGyl2B19a0ejJ2HM0Qd0ISp2Qzu4JwptWzU/PP916AL1ftw739znGlzce+3YDqKQm4f0Az0+sOJhPAmL5NkZVXiAGtfNHK44IMgBds+0DRIo0j0cVEUNPjho+9mp83HMI1XeqbLmTNvP18wzgJCqrhenF4O0z/czeu72adMtCo3bZ1UoX7Ra+rUKiUhZuWQTHtIkEStqu/b2aPkf/d/nt/urLg4C9v5Z6jx9+qXXeU6ikJGNymJoa0qSU8HuuRhAsXUxeI0ypaLYS6vYhHCCGEkOjGsWB/55134rbbbsOnn36KJUuWoEmTJhgxYgRuvPFGNGzYMAxdJHapVKMosnMLaS+2yOHJC253vvjvsUyD88UVZOeLLQ3u6N0EALBk+3Hh8arJ8dhh4HZcUOh1LGx6JIQtBL8d2bVV7QqYeElr19r8fOVeAPAJ9i7P9RPjYvCkSdAxJ9i1otA/P7uPi58z0zps7wTy3ZJMz/LPwdMAzDWuDauWx4FT2bbrbFMnFa9e3d5WWaEGWALiDDIpiBZcRFVk5RcgK70AVcprLSfcjMhupaUGzAV0dQyL3LMxDvxCu9lnwjwqvmWXAABVkxNM3VQS42KQYbC4KcJKcFcfppBPCCGElH4cC/YrV67Ezp07cfXVV+Oll15Ct27mebtJ8VGxak3kyHFIlPLxa8Kj6JzzDk5ArIULhUhNEY3m62YaM6/sPHK7JIUxeF7xJe0TEs1pr/Rjs2BcH/R9ZWFAOf0V5J/VqFdLTlDyjFvhxMfeSGMf7Ej6hVMrYctKExzsk2TUriRJQq2x6L0TLc6d/8J8AL7MDt/f1V1VNsiOBvTPwNddUM6Ic6qnKNv+hUR/eTOLEdH1yg5N8Y0WTvzEx3oAe48vAOvnR5PuLnpfe0IIIYS4hGO72XPPPRcHDx7E1KlTKdRHIetb3K9s15WOBVeJxSQw1ElisKcbTYutTOeD0tiHiXAG5rOD2b2LcNcCxr1RVXHwOL2Q5b//U6/vGFL7hsHzDHzsrd6DXzcewgd/BEYv9wtcVtrnVrXNYywE+x6ZNStyW4mRJLx8RTu0rl0BtVMTz9ZhXMnmQ6fDIkhKEI+Zfp/Z9yAuRlLewY/+3O2r9+xvMwHdbEHBbvA8q++QkSuEEe8u/NeiBIPnEUIIIWUJx4L94sWLERvrWNFPiolu1z2B3bIv+F1skPnsrbS6kTLrrFQ+XrjfSrMp9LE3uYRw+thHGmHwsSghKcHed0V/7/y3v3G1ZLx1nT3hXqSBNRK0jcbMH7DOiNEz1uDZnzZjw/50YdtWtyI53nw8ft142LwCA4Tp7s7+HyuwK5ck4Oou9fDTvRfg/CZVfXU4eI7c+l5IkmSrLrO3V5IkJMb6gnDOXLUPQFHaQTOTetGY+fti93sh+g6pqZQUZ6seP2/O34HVe7QxR2RZVlwttOnuove9J4QQQog7OBbsExMTw9EP4iIF8E1cQ4mOb0aoU8Rg55gtaqYI91uZwoqEFTPCGjwvgmsGD3213pFAVtwk2xXsdb/VY2o3kJm+jj0nMg3zw7/4yxbh/o717EX/P56pta/2C4RWwpaVJvjASfs++GrM2hWNn/p98B+2eozCkUNdgr2FqZ0WMRcS4sTfA7P3XnQNGw/4YiXYdfWx0tgnBpH14+jpomdLlmXc+OEKDJu6BIVeWbMIEr1vPSGEEELcIvgQ1iRqyZd9E8SgNfaWk3bjAhe3E0d81pwf5DRT7QObFF80CbYS5sS+rcZ9kKTgI7QDwG0XNDKuO4IG71+t3m96/Mpz66JOxdDy2hcHWXna51oSCJ5W6B/hF34WC++mddgsp++SXzZNU6XAU/Pc5cYB1jT12rxWvRWDMN3d2cpEQqpa4PVvOgny5pZQaeRj7xS/xl6PmWBv1q5tjb1ubPXfpWAEezWyDPy54wT+OXgau45naJ9xSvaEEEJIqYeCfSnEr7GPhf3Jt5pQNGxPXNTStTRtZqSWKzJbbVAlybSsSPAPpyn+Exe3Qs0KJc+ypUJiHJY80hf9WlSPWB/899UsbdukWf9ofqtvl91754acE6x5s1+T+rbAR3ruA71xQ7cGAftDYVj72rr2jcuKzMXVAqj98S1qxDVTfIN0d05RLwqqMbs2s2uwaxDURudDr188NLIkMENdhdZdQNIGz3NcMyGEEEJKGhTsSyH5Z5MdxIXJx940AJsEJAomqBrZ2oVZZnJCLJ67vA3GDWyGBwc2N9U0i0xgzTTyHin0QHJG1Ue7+74kSRHt439HdsEtPRphrMni0E5dGkWtqXhwnQ/mtGAfY7P3xypyuppgr1WU7s5flchtJVb1/ti1ZAmLf7dkzxR/SJuapsefGiZOz2hmKS9MEaicV3Rik2rlcVFbcft39W2q3aGrslyIGnv1oockab/j9LEnhBBCSj9BCfYzZ85E79690aNHDwDAM888g08//dTVjpHgKYSxKf451ZMtz7eaA5ppryRIiBfMkKskJwhKB0+BV8YN3Rrg7gvPQWJcDB4e3FxY7qK2NYVaSDP5xA0fe6Mhcpp6LzJEro+dG1TCU8NaocZZi4f7+zezPEfdW9s+9qobdOBUNtKz8x31EwBOZubhULpzP3dT7a+DZy/Yx9SsfdH4qffZbVNjBe5iujszAdtPx/oVTY/3aS62SDFbtBAFz/Oj/rw0rZ6Mhwe1EJbTm9rrF1CDM8Uvalx9XyXoFleCqJkQQgghJQvHgv17772HcePGoX379sjO9k1qhw8fju+++w5vvPGG6x0kzimQzgbPEwj27pggGx+TJCPTd3cDOelTkFVIFEeUliCJI32b1B1OjXUovvvFRTR18d5+TTHRQMPqR+QDbpfjGbno8eJ8LP33hGGZygbZGF74ZQvOf2E+TmUF+sqrn3dJkjRWLE5M4V+6oq1x4SAxbV/w7qoX6uwO779HM5Rt13zsATSoIk6BqKZFTfNsBQBwboPAwIfmpvjGdanPi4vx2F/80NWZEBvMOntRJVqNvaT95lKyJ4QQQko9jmcSn376KdavX48333wTqak+n8HWrVtj5syZ+Oabb1zvIHFO4VlTfJGPvRsmmWY1SBAL9hp/T10Fep9XM/9qPwWF2kp6N6tmWFZk3mw2+Y60OXqkiaZLlyQJtS0C+gUVFf/s47Pl0BnLsgUGeez9bDuSEbBP/bxL0GZuMNWY6x48My2umxp7Jd2d4F1R77PrL//H9mPKtnsaewn39T8HvZpVw2NDxFpxwDqtHABUSwm0IGpZS5x1A7AyxdcJ9kG+QaEGz/OqHtMNB9Lx/bqDyu9gA5YSHxsPpGPcV+ux+dDpSHeFEEIIMcSxYO/xeFC5cmUAWu1jXFwc8vLEUZ5J8VIonRXsg0x3ZzUFNF0cMIgobyQQVEtJwAvDtVrJvs2r44EBPhPstrqAU370vrYejyT2bTWwIAh3dPqyvDDgNlZWDtqo+M6Cu9lZB7Dy6xYJ/vogb9qFLbNAbNoOmb9q9h+yiqoc6WZCqkjgVceoqJ7iPChksMHz9JYS8TEe1KuchE9u6Ypruxgv/lmllQMCMysAQMUksWUGYB4VXyvY218U1NcocmGypqgxtbvA2C/WatuKQrn+dE6+LdeKaOCBL9fh69X78ei3GyLdFUIIIcQQxzOJ3NxcbNy4MWD/3LlzUVgYnrzpxBl+H/tgTfEt092ZHJMgCfPKqydwau3Rr2MvEJrK39mnCf478lzMuLWbsB21oOInxsDkPq8gUPCymnyHKviLxtCuNjnSRNuihFV31MPqOD6CG4K94LhemNVEiTcxANC/O2aaVieXOveB3qhbyWf5IPIX9y+OvHpVB7w4vK1msU3dpzt6N7bVnnqxJVSh8sXhbfHTvT0RrzJVT02Kw3djuqNCYmxAeTtxLJrVCIw1Yha40K4pfqwD4Vy/wBNvYoovch3Q4zUxvY82wf63fw6j49O/45r3l0W6K7bwW+Ws33cqsh0hhBBCTHAs2E+cOBHnnXceLrnkEmzfvh2jRo1C9+7dMXToUDz//PPh6CNxiF9jHydKd2drgmcVFd9c2BDNbUWntKxVAVWSEwxSbHlwYYsamrR2ADDpktaomBSHRwcHmuJm5AQGQJMkCQ2rlEeXhtqJcSRk11l394xAq84JtzWDU6wEWG1UfHt1+p9HOwsBVinWRMc1gcwkrWDoP9ZQkKbRmcbePlWTE9D3bNA4s8upmZqIa7vWx3VdxRrxpPhYU7cXP+pvxJIdx0xKWtfRuUEltK4daLnTsX4ldG9SNWC/HY29qEycbmGwSbUif/5NJibY6nsW5wlBY28i2F91bl3hfnVbJUX7DQC/bzqCQq+Mv3afjHRXCCGEkFKDY8F+yJAhWLFiBSpXrowaNWpgw4YNaNasGdauXYsBAwaEo4/EIYWSTxgWRcXX51IW0bG+uXZILWzoUzRJEAtLZtokvabMbF58c/eGWPfUQPRvVSPgWF9B/nUJvon3V6O7a/dHQC3dqrZ1UK/ipLOBFjDaNPZWwndQPvY26wasBXt9IEfhORrB3vd/J2EAN8vuFOHwRvnHRrQw56Qmp4Yn0xb8KxwjK/y9NLvMGIuYAE7QL6qoFxX3p2UZ90FtMRKCVY5+EfPitrUsz1G3Zha5P9rS3WkWukrQggQhhBASzQTaMVqwYsUKdOvWDR999FEYukPcoEhjHyjYP2oSdMrPyO4N4fXKePX3bcLj6mlYj6ZVMHfzUeW3JElC4Uo96Xx/8U4AQHaez6KgXJzjx1CISFg/FUQaM19dofYmurmjd2Pc2lNsVl3Srl3dXad51t3wjhCb4hdt7zmRhTyVYKsIWQJ5Jpwijn9orBYqjM4r+u180PIKvLY06QY9MDwSJ0rP59IDrB6lSgaZEQDtYlKMJDl+Bv10aVhZ81tdjZFcfvunq/HV6PPRpWFl9Hp5gXFbtnpUfKgXGgplGZ4osxIihBBCSiKOZ1o33ngjVq5cGY6+EJfwnhXsh8SsQAqKNE3/HXmuZYRxACifEIt7+p1jeHzd3lPIyRfHUzCKip9b4MWCrUexdMdxLNlxHACw+4Svb/qo+MEiEtL8iwdEyz0XniMMlFYSUWtJ7WvsfYKFG5YbIm20Wgv559nnXTl2Vqjxl3h4cHNIks8Uu3y8dpHLTNF62wWNHPXTL/CK5PozOYHviT8Y5a0XaBeAghkxqzgFIuwomUVxNWzVbaPM/f2bKdteWcbOY4HZDwCt1UeMJ3hHlhiPhAvOKXItUD+bFwqskfy8t+hfAEB+oZnGPshOhQm1BZfThSZCCCGEiHGsKo2NjcUHH3yAe++9F8OGDcONN96IBg0ahKNvJEiSU5KBk0A7zy5MjnsPTe7+DlWTE0y1Tk6YuWofDpzKFga2kwyi4ssyMGr6X8L69IJ9sLKWyKzaaNJoJQ/Uq5yELYetU6GVVMyGONp87K1Sdan761QD74ZyV/SMqS1U9EKt/6dfaxkf48GmSYPPxqew3yEjVwoj/Asgoij1ojGedn0npGfnB0SLD2YxxCploAj/+Jg1ZxbwLlR6NauG6aO6YNT0v3AiIw8XvrpIWE5t+u9x4GMvQv0NU1dTvYJxNoJ0G1ZJ0ZbuThRzoiRQUgKgEkIIKZs4Vne8/PLLeP/997Fo0SI0a9YM9957LwYMGIDp06fjzJnSKwiVJFoOvlPZriWdgMcjuSbU+/Fr3fVzMqOo+GaUC6PGPlht0HOXt8HQdrXwxe3nhdir6MT0FkXZ3NVq3q++Ftvp7hwEzwsGtbCi9yFOy8zDuK/WY+WuNAA+QblcfIwwj7nZpTvtu7+4yKdZpFGXJEmYAs5Os3rhP5j3UPGxNykjErTcyPzhp8rZ72a2gYUSAE1gvxhJCmlhTH09+ktLEWQAAHwBDa2INtm5pGrs3XLzIIQQQsKBY8F+2LBhAICEhARcddVVmDlzJoYNG4a77roLNWsK8oiTYqdm8y44MPQzAL4AesU6FTGIim9GlfLumISrhYnyZxcLrugsjiZtNUGrnpKIqdd3wnmNq7jSt2jDTCiMtqmrpWCv2nYq7LqhgBPlRFentMvXCS4bDqTj69X7cTA9B4D5eHeoV9HwmNOu+595UZA1J4JfMGMWjCm+HzMLAVFquxom2m3HbdsYZbXlRHZ+YYgae/W2pDsmrrh8gvXCaLSJzur+mKV/jDaosSeEEBLNOBbsx44dCwBYuHAh/vOf/6BmzZoYP348rrnmGsyaNcv1DpIg8fi0OLEoDGsEeP2EUZKcC1fl4mNQSZWX3g1T/P/ddh6+uP083NhN7Cbi8Ui4pH3t4Boq5UQiY0AoqHOH2/axd1GF+eT3G/H+4n81+7QaSZ/kYpRf3Wy4m1ZPxqy7e2L5Y/0EJzrrp//9CPXSg9FIB6WVtXGKPm/8b/f3CoguHwp2XoUEVZo6ryzbHh19RpGzLSpb+mfZSGNv535Em8ZeHzyvpEDBnhBCSDTjWLD/4osv0KBBAwwYMAAHDx7EO++8g8OHD2P69Om48MILw9FHEgRSjEqwd6G+qdd3tNcugpv8NKuR4vgcPeo5fkpiLM5rXMU0/ZR4Yu0O0ebTqsdMYClpU9eh7YrSgjnNY++WTPH8z1s0vws1wfNOADB+L6y63LZuKmqmBmqhHVsnmPjYO6vH+TkhpbszKaNeLKmanGD7O6J+P+/u21TZTk7QCs92xlgd7V+WYfsFMopR4kf/vLx7Y2c0qVY+4Bx79zO6vkcFhSXTFN8j+VxZPlm2G/8cTI90dwghhBANjqdosizjvvvuw/79+/HLL7/guuuuQ7ly1pHWSTHj8WmtYuB1JUDY0Hb2tNuSJAWVy9mNPmoCT1lU2KNJVWEO7HDRtVFl60LFSLh8y8OB1bRf7Zvu1NogXIG7RPXmFoiF22AtJJye5X8tQxWkik1j72/PpDl14Donw9iqVgVle9yg5kG17Uf9Lvk09vY60rlBJTStnqyrS7Wt+462qZOKeQ/2Ufz+1W1aEW1KcbVrRkkKnhcb48G3aw/gqR/+wcVvLol0dwghhBANjqPiP/300xg9erTw2M6dO9G4sTg3NileFI29VAg5TELcvrQszN9yVLNPQnBCozYadJCCjqoOq7WFxy5qgRd/2WJeyEXevbFzsbVlB9PYeVEm8zsxm7ef7s5HuJSFdiKV+wl2vJ0uCJilu3PCvwZp38wILt2d9TnqdHdORuOS9rWRkVuAjvW0mQX0ddj5lqmfOVl2dj/1RdXfPiPXjXLxMUBm0W+vbD1W0SY6qy04Qom/UNx4JAkbD1BTTwghJDpxrLE3EuoB4NZbbw2pM8Q9JJWPfbj4fu2BwHal4CIHu6FBVs+DRYsDI7s3BAB8c+f5tiJJh4K+/couZyUIFTOhMMrkelOh5HxdcEOnpvjhEnlEeeHdxqlhjGKKH6IgZScNZMtaWpP4ApMc60YUmeIbX2icahAcCdSShBu6NUCr2hUsylnXpb4PMsQ+9m9c28FW/erfI87zxQcZ3FoblPaBAc00v/ecyLTUyLsZU8INzLJGRDPRtuhJCCGEqLEl3Vx++eVo0qQJXnnlFXg8nhIXXKtMclawj0EhCsJ0u/IEfrMSpKB8cN03xQ88PvGS1nh4cHNFqA+nCWi0+9ibCYX6KO6Rxuw2fabzU7ad7u7s/QnXpTp5tDJzg1t8c2rZ4lE09uG/v/Ex2vgVBSGEPje7pWp3mlDSzPnRj4ydxRNJY4ovXjS7tEMd8bm6PqtPrVc5CZufHozEOO0HdXinunjgy/XK721HMizvaXS90dr3oyT52BNCCCHRjC3Bvnfv3qhVyxegqn379pgyZUpAGVmWcf/997vaORI8UqzPxz4WXuSFaSFGVGuwGnvNZDjoqPjafohQa+rL8nzSbHGuIIhAZ5FC74dsN76D28HzAN+4+aO0O1nYOZ1j32xfg+Oo+L7/iyMKuf760zLznNdhJyp+kBp7u9hZxNaa4gdq7OtVNo5BE6CxV53tkSTEx4lXSRNiPZqYDYfOpk40Ipq14iUpKj4QfdYPhBBCiB9bgv19992nbD/88MPo3bu3sNzDDz/sSqdI6Ph97GNQ6EqubicEExXfjT5qfexLXgqoaKFVrVTM+edIpLsRFHYXlYp87N17CL5bewBXnVvPV7+g2rgYCfkCk/TTNv3xkxNikZFbZOLv2BTfpXR3Iu7q2wTTFhSl/NO3cTLL+eKFncWR2CB97I0I9Hm3Rn0fvF7nLgFGDZrdX/1zu+2IuXvE6WJwDXGCuvvRvOggomT1lhBCSFnCsdH06dOnA/ZlZGSga9eujI4fRUiST7CPQ6ErJqr22w0uyrcbPbQyxddTXJqXNeMHFEs7bhFbjNkC7GH/PjkVdt0U7NUB80S1GuVXtyt0fX9Xd81vp++ZOt2dP9Vj+fjwpHzUX79RIDg72I+KHw5TfOs6g42KD5gvJJi1XTFJG7PDTgC6fWlZtvsVbtSLNiVPYx/pHhBCCCFiHEcQmzlzJu644w7NvuTkZMyePRuXXnopLrvsMrf6RkLAr7FPQD4qzHsICCJn+/K2OZi3+SjqVU4CZv2O52L34oBcBe8WXgIvPMIZtwQpSI292lc2ONR57O1MyIsrzVK0Bc6zIi7KBHsnt8l2qkXFFt95f4xQC5aiRaPUcnE4nhFokm5XY68P+Oj0LqnT3fkFq2opCcg8EX6BL5iYE/ZM8YMI6GGCPhaAreB5HrVgD0c3JjB4nr3FyScvbomxX6wrateGYL/7RKbvWx4FqO9tnkEaSEIIIYQ4w5Zgv3fvXuzevRsAcOrUKfzxxx8BE9eTJ0/i1KlTbvePBElCckXkyzGIkwqR9PcnQdVRE8ANsQBOA1h9dhvAWvkcLPO2Fp4jScEFQ3LDP1ZyuDhAzYuYGJ2wVDFJrGmORpJsaqDtpLuTJGfPiPqZc6Kx79qosr36dQ+100wS/gU3WS66rvb1KmL3iSxUcvke6/8+BPOuKVHxTa7TbR97vZBpK92dJKFBlSTsOZGFro0qBfTD7NpNLPFNr/vSDnWwaOsxfHs2M4nZc1whMRancwqCykxQHOw6nol2dStGuhu2ifbAqIQQQsoutgT76dOnY9KkSQB8kw29j70kSahevTqefPJJ93tIgiIltTL+7vM+Uk6sR6Oqya7UuX/B+6grHUeFs0mUhcHzEJyJuxtmtFpTfDsa+5CbjDqmXt8Rj3z9NzLzgk9zqNfYR1p/7+Q2JcXH4uNbumJvWhbGf7/RuE5FYW9ce4wkoUD1LNepWA4HTmUblrd65CoIBPuUxFjcekEj8xP99ZtEULd1/tkTftpwSNk3pk9TdKhX0fbiAgC8P6Izbv90tWkZ/agGtYh29hyzy9Sa4gfRhg79N8FuursPbz4Xq3afxPBOdZGTb//di4/RLqI5uYZcVZDLFbtOGJbzu3q8Pncb+raobr+BMKIe5pKksZfl0vl3gxBCSOnAlmA/YcIETJgwAQDQt29fLFiwwJXGf/jhB7z77rvIy8tDbm4usrKy8NBDD+G6665Tyhw9ehQPPfQQduzYgaysLJxzzjmYOnUqqlcvmqCkp6fj7rvvxtatW1FQUIBLL70UTz31lEa427RpE8aMGQOv14usrCw8/vjjGD58uCvXEa2063slgCtdq2/3vB9QN+Y4EmBsOixJUlD6DG1E++Bm6Fr/VOvypSW68f/ddC5u/3QVXr6iHYa2q43E2Bjc+smqoOvTmze/eV3HULsYEka3yegx6d2sGo6eyTEX7G2ku/N4JE2BD24+F5dMXSIMgAfoBFBBkViPhPLxMZpFl1t7NkZCrD0rA7Oc53YQBRZMSYzFqB72Fhb8VK+QaFlG/26Fy+0lGLcfJ9j5FlUuH4/qFRLRtHoKAAQI9maa8v/0bIz1X6zFlZ3qAgDKJ9j3jntgQDP89LdvkeaTZXssy/+9P9123WFHNSROFkKigeL4s7F+3ynEx3rQslaF8DdGCCGk1ODYx/7bb791rfF33nkH119/PW666SYAwKxZs3DppZeidevWaNeuHbxeL4YNG4aWLVtiyZIlkCQJd955J6644gr88ccfSj0jRoxAjRo1sHLlSmRlZaFr165ISUnBAw88AAA4c+YMBg4ciJdeegk33HADtm3bhs6dO6Nu3bro2rWra9dT2smFz1c8UTJOXeVxaL7sx40Af5LDxYHi8rEPNwNa1cC2Z4cg7qz2L1RhR60F/fPRC1GnYnQGxXygfzPDY3E2fa/NFnfUgrBHAlrWqoBNTw/G+O834ou/9gWU1/jYGyxvpZaL0wj2g9rUsNVPQBRozWHwPEHxYNbQ7GQeOKWLgh+cwt53lmnwPE1UfPeFfKtXqUXNFCXFodIPXYcPnzZORXdxu1ro26KaEj+hlQNBrkm1ZLSrmxpdAnsQlLyvcHh7nJ6Vj0un/QkA2Pn8RfbjhhBCCCnzOI48tH37djzwwAP44osvlH2ff/45PvzwQ8eNP/fcc7j++uuV33369IEsy9i5cycAYOXKlVi5ciXGjh2rTJbGjh2LJUuWKIL933//jVmzZmHcuHEAgKSkJIwZMwYvvvgiCgt9E+jp06fD6/UqbTVr1gxDhgzBSy+95LjPZZkc+EyJ/Rp70fRGkqSgNOFqOcydCPnWZcI5PSvuNYM4lXAR6kRQbYofFwWTSiMh2ew642LNP21GeexrpxZpo9XV+xdL4mKM61WXF99/KcAcP9Gmtv7s6dqfDm+NyF88GGHYzppJuj4gYDA+9oopvomPvcum+HqsfOxFMR2cdkMdFPHKznVx8/kN8NrV7W2da/Y8RjPqd7qUrK+6xrGMXGW7pGUMIIQQElkczwqeffZZZGZm4txzz1X2de3aFfPnz8fTTz/tqK7OnTsjNtY3qcnPz8crr7yCVq1aoX///gB8QfsAoEaNIq1WrVq1AACLFy8GAMybNw/Jyclo3ry5UqZLly44duwY/v77b6VM586dNZqULl26YN68eY76W9bJOauxT4Evirab+Ydd0dir6rBTn6j7t/Z0ZpYcjdjN5W6E25HGI4FVZH+jPPaXd6qjbHs0gdmKttfuPSWuVBMVX1wkWWdqnRBnf6z1z7TT4Hn1qwRGRA/mUbHTrt78PJSAY+Ya+9Czaai5vVdjAMDdfZvaqlPkRhHK65cYF4NJl7bB8LOm+VZEftktONTvR0lziQp3d9XPTzCBaAkhhJRdHM/gT5w4gffeew9NmzZV9jVp0gSffvop5syZE1Qn7rrrLlSrVg1z587FnDlzkJzsC/bWsGFDAEUCPgAcOOCLArx//34AwM6dOzWCPwDUrFkTALBr1y7TMunp6UhLSwvoT25uLk6fPq35R4BCySfY3xE72/fbYIYTzFREPZkJdiqjMcW38WRXS05Qtnc+fxF+v78XHr+oZZCtRw+hyuWanONRIDk49bEHrE3xjTT2auFZ7dKgHpLcArFPsFVUfEkKtDJworE3i6Buh/MaV8Gg1trvYFCm+DasOPJ1aeNCiYpvhtoM3o0AnI8OboHf7++FBwc2s1VnrYqB8QbC4RJghJ1L/u9I3yJ8o6rlw9yb4ChZoqscfsFetV1a3MUIIYQUD45FgLw8sX+1x+MxPGbFtGnTcPz4cfTp0wc9evTAoUO+gEBdunTBeeedh+effx65ubkoKCjAc889h7i4OMXMPisrCwkJCZr6/L+zsrJsl1HzwgsvIDU1VflXr169oK6rtNGmjc889LicCsBYYx/MZEStBayaHFzed8lg24jbejVC10aV8cLwtvB4JJxTI8U1f8ZwmAXbJVSNvRMtcnFg9DSZCVBW97EoeJ62dvVp6mdSs21Qt0ejsbcRYA9ONfa630HcZn0wrqBM8YPS2DvHP4amUfFd1tj7vwN+gd7oUlvUTEGHehUxpk/TgGPF+e5nWWS/6FS/Iiol+b6nBd7oiT6vfh5Kmuwa7nR36sUkauwJIYQ4wfEMvlq1apg8eTJycooCAuXm5uLVV19F1apVg+5IbGwsnnnmGXi9Xrz22msAfH/gfv75Z9SrVw99+vTBwIEDMXjwYDRo0ACVKlUC4POpz83N1dTl/52UlGS7jJrHHnsM6enpyr99+wIDZZVFanYcAgAoJ/nGrsBg0lG/cuCYWqGeDFdNTjAuaBM7wket1HL48o7zcV3X+iG3pyeSk9UA/2aHqM2Li1P7aISRkOzGGkxAzUbCvGo71lCwN29LQuBzaTcivq9rkulvO+jbD5fGvqAwdI29gk1T/HA8qkbfkeu61sf3d/VA0+rupBINln8OmluTPX1pG8W1pjCK8tir3+mSpJWW5WIwxVdtR9FaDCGEkBKA46j4b775JgYNGoQJEyYo/u6HDh1C7dq18euvvzqqKy8vD/HxRdpZj8eDZs2aYdOmTcq+SpUqYdq0acpvWZYxZswYtG3bFgDQuHFjHDlyRFPv4cOHlWNmZVJTU1G5cmD+5oSEhAANPwGkeJ8pZ3n4FnWMtAl1KyUhPtbjKD+xepEgMc5BQDFNB1WbkZdHI0Z2iOmjEqNMY28kwFrd4/Z1U7HeKGK4YopvrLFXxyVTt2Uk7Fm5kyTGxSAzr0D5PbBVDUcZDMIRVDKYOu1YhOS54GNvzxTfXY29HqPbk19o/G2Ltm+P3yvFaCGWOCPco6jxsS9Bix6EEEIij+MZfNOmTbF582ZMmzYNl19+OS6//HK8/fbb2LRpk8bv3g6dOnUK2OdfJPCzcOFCzfG1a9eisLAQF110EQCgX79+yMjIwLZt25Qyq1atQvXq1dGuXTulzJo1azST+FWrVilB+og9pASfdqocfBp7v2Dfrm5qQNmbzmsgrKNiUpxwf65KGBVFmnaK08BipYlQ54IajX0UDOPAVjXQUBT4zUKUa1XbOHVYUfA84zpjbGjvjc4V3YPEOI/m3I71Kxn2T1i/C/fCDa2/nVPc0NjbiYpfPaXIxz0ccqtR22Ym0vpzjCw8igNJKgqGGU1m3aGY4mfmFuCrVftw9IxxGsFwEn6NPU3xCSGEBEdQqrn4+HiMGjUKr7zyCl555RWMHDlSo3m3y6ZNm/DTTz8pv2fMmIGtW7fi5ptvVvbdfffdSgT8nJwcPPbYY5gwYYKiaW/Xrh2GDRuGV199FQCQnZ2Nd955B4888gg8Zyc0o0aNgiRJSoq+7du345dffsHDDz8czOWXWWISfYJ9vFSIBOQp2gSRoGPkO6zPb+0nJ79IECipKZyihVB9QONVqeKiQK5HYlwMFozrE7A/FEHXv8hnNkn3GATPM9Sya3YHVlwuLkYjSDuV99xwiwgwxQ+iDlum+F69xj54zO5zTVV6wjM5BcYFg23b4FNkpv3Wj89dfZ0teLuJBEnpTzRpfzVR8R0+HVMX7MBDX/+Nx7/d4HKv7BFuH3t1/SXJTYEQQkjkcWyKDwAzZ87E22+/jYKCAvz555945pln0LBhQ4wYMcJRPW+88Qaee+45vPDCC/B6vZAkCT/++CN69uyplBk0aBBGjRqFOnXqwOv14tprr8Xdd9+tqeeTTz7B3XffjW7duiEvLw9XXHEF7r//fuV4SkoK5syZgzFjxuDtt99GdnY2PvroI3Tt2jWYyy+zJFeojAw5EclSDupIx+H1NgEg1kipczPbwSjSuBPUgg819sGjdqEonxDUJ8J13Ih4rsY/RvqJs5HJvceGQK6Jii/U2Mdoyjh+Rl0YggBT/HBFxdebqodJQFEvAuaE6IIiwuhK9cEB1ejH584+TVzskTN8Gvuzgn0U+dircfpozPzLF/Nm7uajYeiNDcI8jOo1I7XG/ulZm5CenY9Xrmrn+veQEEJI6cDxrP29997Ds88+i8svvxxLliwBAAwfPhzjx49HWloaxo4da7uue+65B/fcc49pmVdffVXRxhtRsWJFzJgxw7RM69atsWjRItt9I4FIHg+OyhWRLB1GVaQrWivRRL+8wJw+tVycYWA3tcbelb6W4XlPt8ZVQjrfnxYrPsYTfLyDYsCNxRv9HF0t6GsirjuNii84fnmnOnjply2qOp311Y1nOlBj77xSkTuNvp4Dp7I1v53KQmq3Kbs9DDW2hAijZ6zQZlSzyzvWifg75P8+R5OPvWywTbTfoBMZeahdsRwKvTL++6cvfe/YfuegvsA1iRBCCHFs8/zpp59i/fr1ePPNN5Ga6vOtbt26NWbOnIlvvvnG9Q6S6CL/7FpQrFSopLsTCfZqE1k7uKKxtxHgrCxQIyW0wI/lE2KxdvwArH1qgEs9cofezappfgdzi288z5cBwT911gfPU2tiE1QCmcYU307wPIG00qJmBVu++uEkoMkguiCK5N+3hfbeLNx6TPM7FIW9Xe1kOPyRje6RXSE50sJ0QqynyBQ/igR7hBAVP9Jf9nCPovqbNGyqT3mivnfR5FJBCCEkunAs2Hs8HsW/XT3hiouLCzqPPSk5+AX7OBSaauwHtKqJyzvWwfihrZR9RmnLACDXQQR9I7RmziFXV2JxQ2CsVD4+aszw/bxyVXtNxH7rqwwskZLo0zYbmeKrTcjVbanH1CgGhFZjb5DHPiQf+9DRv6turC1c360+OlkEAjR798XlnfcjIdb92BxG42NXSNYHESxuGldLVixP8iLcFyOc3utIr9mqn+XHv9uAdIO4McGif7QKCr2a560s/20jhBBijuOZUG5uLjZu3Biwf+7cuSgsdN8UkkQXBfBp62JRoGgORIJ9jEfC69d0wH96NrJVrxv+sWqhqSz7IJbWS6+WkoDpI4viYgQV0f3s/37BWy9U5Ks09rEGwfPUKdbU2AmqpbEqcThDd+OZdiN4HgC0r1cRAPDQoOZ4/vK2kCQJN58vzoQBBGGKr9q26mPVZJ+FyhvXdnTYijWhCvb5EfRrH96pDgAgObFoge54Rm6kuqMhtFGJ3AdOhrbv/1uxF/9buRdZeQWuWUToFxszcws1Wno3gmgSQggpnThWyU2cOBHnnXceLrzwQmzfvh2jRo3C1q1bsWbNGsyaNSscfSRRRL4i2Bdi/tngRXbyWlvhto99pImksWRpXtRQy8LBaK78Q1OksdceL1D5ThstFBlp7NVu1/oFA//p6j47vU9u3NXA4HnB1frJqK5YtSdN4x7x+MUt8dOGQzieEWi55VQrq/Gxt+jiT/f2xJbDZ9DrnKrOGrFBqKb4dn3xw0H82edUHcg0zwXLKDfQRMV3aoofwc+bLMv46e9Dmn3ztxzBG/O2oWn1ZMy6u2fI39+jp7WLL/leL9Q6mFL8eSeEEBIijjX2Q4YMwYoVK1C5cmXUqFEDGzZsQLNmzbB27VoMGBBdPrnEfQrkIlP8SmeDaNn1+TMLbhWOiNak9OHRSsaOz9dru/RChVrDqllEUH0pjfKSqzVtRm+Enej6RoQlj32Q9aQmxaFfyxqIVS1yJMTG4Nou9YXlQ0p3Z9HLGhUS0btZtbAsaBkt4tg2xY+gX7vassTvphCN3tmOTfHD0w3b1KigjR/z1+6TyMn3YuOB0664lGXlaf8WFnplzd9Yp5Y+hBBCyg5BOdG2bt0aH330kctdISUBtcb+YHoOAKBHk6oBwbKE55qYpbriY8/5TqlHI9e7UJ9eqFD7RBulTzQS9jSRvvVp9AT1OI2FEJY89i6/M4dP5wj3O/axd6MzLnHPhU3x1vwdmn0FNjXxZmnxwo36OfXfZ2+UBNBTu6047VGkv/Nmf6vcyDufnV+g+Z1f6C3TwWAJIYTYJ7qiY5Gox+9jHycVTT5a16mAF4e3Ra2K5SLVraiD07DwoDWPD+Z83/9+QdM0j71KftcK9gY+9iaTen+/tZkb7PRY3LdgCTDFd/lJTcsUB1B1boqv+hHhl0mUrs6uwG53ASAcxMeIgz8WJ0fP5OC2j1fhuq71cW3XImsO9f11HhU/sg9EnkkGFzf87DNztfUXFMooKCz6exstizOEEEKiD1um+J07d8b111/vO8HjQUxMjOG/xMREnH/++diwYUNYO04igyLYo2jyERfjwbVd6wekIxPRv2WNsPWtVa0KAIDK5ePD1oZdOPUKD/UrJylmxedUT3F8flHwPO3/ftSBw9UChFouMppXq/cbySqRDvCoF/DcjiRvuOgRwhsRaWXlxW1rBeyzcj+6qnNdAMC9/c4JS5/soDbF92+5oVF2wuu/b8f6/el49Fvj+UBJi4pvrrEPvf69aVma39uOnMGM5XtCr5gQQkipx5bG/sknn1RS3LVv3x5TpkwxLFtQUID169djzJgx+OOPP1zpJIke8lVR8f0Y+RyLeP7yNpi7+UjA/qrJCTiekYuqycEL5eUTYrFx0iBD4YKUfKomJ2Dl4/2RU1AY4OuqRygAnN1plO6ueoUE4flqgbh6SgJEaH3sjUzxxXXawR0f+6Ltb8d0d91fN16Q4x4IQmMfRUtjDauWD9hnpTV9+cp2ePyilqhUjIuMPZtWxZIdx5XfcQKNfXGnQM810G5rguc5rDPSX3eztIFOXU5EvL94p+b37Z+u1vwu7sUZQgghJQdbgv3ll1+ubL/44ovo3bu3afl+/frhhx9+CK1nJCopUOWx92PkcyxClBoPAP53Wze8MXc77h8QmoYrOcpyr0eKp4a2wgu/bI5ouq1wkZoUh1TEWZYTZWtQp7v7deNhPPGdNnXnnX2a4MDJbFzaoTY+WVakJVM/tvdceA4+WLIroG7ZhsY+pOB5Log06vcvHO9KvI34A3ZQj1+kBTkRVq+VJEnFKtQDQNPqyYaCvX8Qi1sojPPYeB4cR8WP7BNh1l03TPErJMbidE6B4XHK9YQQQoxwPLMbNGgQAGD58uXYvHkzAKBly5Y477zzAAA5OTl44oknkJmZ6WI3SbSQf/aRiVUJ9kZ5vUXEqiZ6F7WtqWw3q5GCaTd0cqGHBABu6dkIO49nYMbyvZHuSsQQLSKpZYLRM1YHHK+QGIc3r/PlQ/90uVqwLzoxNUm8qGDuYx/YvlONvRPLGCPUbRotsoVCvIFpfyjCSKQFORElwc9ZbbmkaOyLuw+x1vcu+keyCKuFUjceC6vFAWrsCSGEGOFYsN+9ezeuvvpqrFq1SrO/S5cumDlzJurUqYNLLrkEd911l2udJNFDvlwUFd9PrIFWRkSMarJ5d9/I+Z+Gm/LxMbDOExBe9EGYyhpmgrB+bly/chK+vON8zT6PQ394Oz722jotq9Se65GwcFwffLtmP4Z3quvsZEGbbiwU6DEy3okm03o3iEZ3nz93HMeSR/qi50sLAAA5+aoMD7qgkcWF0d8GdT9Kkpxq5Frgx43xtYrfUALWlAghhEQIx5GTbr75ZrRp0wbr169HRkYGMjIysG7dOrRu3Ro333wz4uLi0Lt3bzRu3Dgc/SURRhQV34mAUD4+BoNb10TXRpXRrEay6/2LFt6+oTOaVCuPd2+MnBXCGRNzzrKAehGpUdXymHp9R8WcXT83blc3FTVTtT776qfaziO+TxX0Sl+/v11tnc6Fw4ZVy+OBgc2Fft92CLfG3uiaQomKH20idLMayXh4cItIdyOA7UczUEeVmeTomaLUg5HysTey4FDjOCq+6oH4e/8phz0KDSuNvZVQbgdrc35K9oQQQsQ41tinpaVh0aJFmn3t2rXDf//7X7Rp08a1jpHopACBGnsn8okkSXh3RGe3uxV1tKpdAfMe7BPRPnSsX1EYqLCsoF5wmvtAb8R4JLw5bzuAQAFHpJGXpEBTZjO2Hc1Qto00d6HksXeDcAv24biiaLDEr1EhAUdO5wIAfrvfPMZMJFE/s1l5qm/02f+LW9trnBpSte2wTvXzcOvHq7Dyif7OOxYmwmmKX7l8PNIy86ixJ4QQYohjwb5evXqQZTlgIizLMurUqeNax0h04vexvyNmNm6J+RUAkPiOx3T2vSmhEKdQHv/Je6hY+kh8/KdnI+w+nolT2floXLU8TmTmITu/EHf2bhLprhULajNgSfe/XpwQybjaCPbW7cV5JDR89CcAwMBWurSOZ89XWyaHQa62RN1mWAR7Q429M2kk2kz37Wieo42sXPXiq99SpZiD56l8M3ILCpEgyJrgVMmtXpw6eiY36L6Fg1BjL8iybCi4+1/XkuS6QAghpHixJdjv3VsUgGvEiBG48cYbcdttt6F+/frK8XfeeQdXXXVVeHpJooaWnXvDu+5nxEmFRZHxLSy+kyQgCbno6THOZUzcJzEuBpOvah/pbkQMtcZeFLxOjUlmvLPb1kLwvC1Hle3fNoktJSKdx755zRTEx3pQPSUBlZKKL2p7aKb4kVfZG0X7jyZevqIdAOCm8xvgk2V7cPeFTZVj/lfBa5ypLSyoU1LuOp6JFjUrANAuMDhdbCjup2GHyhLHilAD2xlp62/p0Qiz/j7oShuEEEJKL7YE+4YNGxat+J/9o/L5558H7Pvmm29w6623hqOfJEq44PI7kN5zKPKyM5BfKKNKcjwSLLRZP712Gy6OWYlYFPOskpRp1D72gRZG2rKi3NShpKbTI85jH1qdwdC4WjJWPdkfCbEeR2kq7WK0VhGKKBINpvjxAk1ztHF1l3oAgEmXtMajQ1ogKb7oz7t/DIs93Z3qGTNMBxmCxj7c5BYUov9ri6wLniVUM3mRj/66pwYgtVwcZlOwJ4QQYoEtwb5bt2744osvTMvIsozrrrvOlU6R6Ca1mjOXi3TZF+grBmU7SjspXkRBHSWDIGIZggwCagEiVFPs3ALfwoFa+xwJH3vAl9IvXLgWPM+FvriJ1eJlpPHorEvUQr3veGSeNbULhpFA6vReN6xaHjuPF0863Zw8Z4vR4dDYVzxrWROpAIiEEEJKDrYE+5deegkNGjSwVY4QPYX+gHsSBXtSfMSYpGHUm/82FkWZV8lCiXHuaGw1GvvolhWDol/L6vhwya6A/U7NrYs7LZsVN3dvgHUzT2FQ6xrWhSOAlVtHUfC84h1XjWLeIGCeHb/0Exm5qFw+HpIkFe8ii8P1kFB97M0i4ot87DNzCyBJCFjIIYQQUjax9degV69eyvaqVauwatUqnDx5EpUrV8Z5552H9u3bB5QjxI8okj4h4Uassff9r5dvRPmpNRp7A7P1TvUron+rGnj51622+hRpH/twc37jKsL9oWjso2GYLm1fBw2qlEfzGimR7kpQGFmqhB2D9jR57C2q+HXjIYyesQbXd6uP5y9vW6zX4NRdJlRTfLMYCP576F+c8Xpl9J68ECez8rD56cElMsAjIYQQd7G9zLt582aMGDECa9as0eyXJAndu3fHJ598gkaNGrneQVLyKRLs6WNPio9YQaotxZxVt18kLKjPFvngA0DXRlXw71H7ZsGRTncXbgyj4juo4+iZHLy/aGdRnVEQPM/jkdCpfqVId8MQqxGKhI/9T38fwvfrDii/jZq26tLLc3yLZv9bsRfPX95Wcw1Vk8MbANLp4luo41ugkuzb1knFff3PUfXF97+/hez8QhzP8GUFWLUnDd2bVA2pbUIIISUfW0u8e/bsQb9+/dCjRw8sW7YMp06dQkFBAU6dOoWlS5eic+fO6NOnDw4dOhTu/pISiN8Unz72pDgRaew9BgKOaP6uPr13s2rCNtrXTUWOQNtvRGq5OOF2aceJaf2YGWvwgcCcnwSP0YJWuMgr8OKu/63B0n9PKPu0kfAh3C9CvwCm1oqHIwCkUzZOGoQaFRIAmJvS28EfPM8jAbPu6Yl+LYtcP8wWZ5apxpkQQkjZxZbGfuLEiXjttddw7bXXavZXqFAB3bp1Q7du3dC9e3dMmDAB77//flg6Skou+TTFJxFA5GPv1/4Gzo2Ntfu+urTH5z7QCxsOpGNwm5r4Zs0B/amGjOzeEOUTYlAxKR7t66baPq8kIUmB4+tEkblqz8mA+og5VmNU5IJSPKK9SMA1ioRv1SX9pWnM+KMgFINHci+wnX/c9N8bXzvaNtQCPl8RQgghgE2N/datWwOEej1XX301/vnnH1c6RUoXhWcfMwr2pDgx97HXzsCFvrSqffqJdtPqKbi8Y11IkoT7B5wDu6QmxeHWCxrjys51S6WPPSB2MXAaPE9N6Ryl4qW4I6qLHu1gm9Y/T5rAe2G6oCOnc7Bg61FbCyESJKWPofYnJ99nii96h4ruoa8N7TiE1CwhhJBSgi2NfWJioq3KEhISQuoMKZ0UyDTFJ8VP1eTA71HRBFy7X2yKr9LYmwjhzUpoQLVw4ZGgvOmVkuJwMis/KjSrpZGUhFicyS1A+7oVTcsVRcUPe5cMMQqYZyUM6189r0E9btL9xfko9Mp46Yq2mv31KpfDvrTsgP75jYNEeeidMG/zEQDimB76eyirioSycEYIIaT0YEtjn5OTg8JCc6GssLAQubm5rnSKlC4KGTyPRIDuTargzj5N8NrV7ZV9Rj72ItTyhMckPLbIMqAso14QubRDHQChCWCl1bLBDb67qwdGdm+IaTd0Mi1X3Kb4IrSp7+yb0+vvv3pxIlyX4zeJX7z9uGb/q1d1wLVd6mn2eSQpQJv+5V/7cPsnq5CT72wx299ueUH6Ov091MQsoFxPCCEENjX2AwcOxGOPPYaXX37ZsMyTTz6JQYMGudYxUnrw+9jXkE4Ce5dHuDekrOAB8Egr/H979x0nRX3/D/w1W+6O4ypw3FGOLr0fR5F2NAlihdiwGzV+Y4zRKJpY0WCJFWPLL4kVSzRg0BAkEUHBgtKk93Yc5e643rd8fn/szuzM7my723r3ej4eJFtmZz+7s3vue97vz/sDoAI4fgIAkF15CkOkIgh7jnZbH6WvgO/gXZIkJBgNXjvntzV6nf9bEngwrPeuX+cUPHrREL/bSV4qVSKpqLxed2UBq59BqY+/EAI/FVZoroeT+7r0g7qk4sl5w/Dhj4Wu8Umuih75T8DCZdsBAG9/exS/nNpXd9+NVhvKay3ISXdVRMrP9rOhOR7bux9DzQmOgF8RERG1ZgEF9vfddx+mT5+OMWPG4Morr8TAgQORkpKC2tpa7NmzBx999BESEhKwZs2acI+X4pDB6Oj+PdW4HXiDJ38oeuYAmJMIrCj7BVZihnK7XvCo7r2n18xKzWSU0MSZJgC0/QpcS3QFHnroNd+jljFEOGOv9zR3fLAVo3pkoHtmsubT8MEPx/HkvGGeD3BSfw/f/vYoKustrucJwVh9cW8CKEmSRwWBQZK8dqwvq2vyuu85L67H4dJafHH3FPTrnKp5vO7fI7fvkvq5IrmMIRERxa6AAvt27dph3bp1eOSRR7B48WJUVlZCkiQIIZCWlobbbrsNixYt4hx70jXvyl9g/yffo2e7BiSaor88EbVdDdVlSLJUIMtSqLldv9w78DXnHctuMbIHtO+VcimIuMMgSZq5yqzEb7lIL3fnzcHiGkdgH8BAhBB4+cuD2FlUpdx2pLRWs01ZbRN+OFKGsb07hHqoAHR6cehso+6K7x5g22zeX+hh52v5fOdp/Hq6I7CXH+6r50dJdSMe/XSX5r+l7pUFRETUNgUU2AOOxnhPPfUUFi9ejH379qGiogIZGRkYMGAAjEZjOMdIcW7g4OHA4I3RHgYR9nz4OEbtfRYVtdp+IP7Wsfc3j16dRWzr1P0IXBn7wLm/05xjHzqRyux6q9AI5HtitdlhMhqwetcZPPe//Zr7tjnL8C8Y3gX/3n4KAHD5X77D0afmtmzAXrhXOOh9FCXVHHu722wcf9MM5Me783Ui8V9bi7B2X4nmNs4CIiIiIIjAXmY0GjF48OBwjIWIKKyE8wdzk8V/gKH+ce2v27Xc/Z3cS/G1TcUCwTg+9CK93J235/n+8FlcPLKb18B/z6kqXP76d5gxqDOGdkv3uL+0xlHa7m9qTKi4nwiRvHR8kE9muW8fUJNO1S7lzLuvjH2dzpwfluITEREQYFd8IqLWoNHm+HFscAss9DJkp6salMs/Hinzud9zuOSdQq8UP5i4w1vwRM1X22QFAFQ3WKM6jnZmRy7B2+dh+ZYTqG604l/bTupmsi027+u8h4O3ZTHdK3i8rbYRSMZe/VpUE1A8t3P+WjtUUutxHwN7IiICGNgTURtid/7JM7gtvagXJuT19Ozg7Q17R7hoArJmlOJDk/EPxYjo2Nk6AMDvPt4Wkefzdrz9BaDqknK9Qx/5wF5/vO7P722OfSBz39V7kh+vV5DQZHW89tIaz2WFGdgTERHAwJ6I2hLnD3Cje2DvZ469v5/NjuZ5BADqt0LOvgcTd8gBDIVegyUy7623qRdyl3n3ux//924A+gGtmtXZjC5SX7f1buvYy38npvTP0tyulOK7vb3uXfX1aDL2PprnXTqqu9d9sHceEREBDOyJqA2xw9Ho070UP9gGVu4SGNgrNKX4zVjuTo0J+9blpxMV2He62uP2v284gp1FlThQXKPcpvf1szgj50jNsffmkQsHw2SQMMA5BcdbKX4ggb2kcwJR72/PxH4dve6DXfGJiAhoRvM8IqJ4ZZfkOfb+S/HVwX5GO7PP/ZpZiq9o6Rx7NXbEj0/eDvf2E5WY/eLXuvdd8OcNmuv6pfhyqXp0PhdyBUpuh2RsuG860tqZNOPxCOyD/ODLlQ56ry7J7H31IZbiExERwIw9EbUhwvknz70UX++XtDopeMXYHj73azYyAJUZ1KX4LXxb+K6G3qLPdqFep7N6KIUrzpQz4NHK2Ks/zznpSUhOcAT2RiWwB46UuprbBbvcnasU3/P1+erjwYQ9EREBDOyJqA2xS/ql+HoZQPUtSX4y8mye5zKiewYAIDst0Wsmk6LnzW+O4rWvDkV7GH75Clajl7H3cruqFH/BX79XbvdWIq++XdvLw/tyd8zYExGRPyzFJ6I2wy4CL8VX8xdIsHmeyx8vGYqCAZ0xpmcmlm85AaAlpfghHBgpCsvqwvsEIYgzfc1Pj17G3ss69s7b1+8vxalK1zKZ3gJudYm++m+L/JL1lnz0mbFnyp6IiMDAnojaELukv9ydPwzsA5eRnICf5zk7eDvft+Y3z2NkHw7hflebe7zVfJWxRy2w93J7iXMJunYJ2qy6zcufGbm7P+DWPM9HV/x0H30+7AL48WgZKussmDk42+t2RETUuvHXKBG1Ga459gGU4qtuM/j5S5nAUnxdLW2eR+ERD00JfZWXR60U38vTzhmaA8BzmT9vy/7tOlmpXFafpBA+1rGXJAmXj9Ff8s4uBC57/Tvc/M4mnCgPczUGERHFLP4aJaI2w+4MNQ2S/3XsLap0WzAZ+3M6p7RghK2La7m75u4gVCNp25ZcOVJzPZC4+Pn/7Uev+1fi9WbMxw/FiRx1VttdtApkvJ0QkVTN89S8nZyobrQqlzXr2Pt5Hm+VCuqnUU8FICKitoWBPRG1GTZlHXv/c+y/2HNGuewvsLfZXfuTJOD5y0c0f5CtiFxK3+w59iEcS1vWu1N7zfVAKtlfWnMAAPDUqr1BP18oCjQq6y1e7zNGIWPfz8cJO6/r2Ht5IyxW198L9WN8LXfneB79e9T9CHydECEiotaNgT0RtRnC+ZPZc7k7zx/MnVOTfN2tMa53R9e2kDBvdHdsfWgWHr5gcPMH2wq43jcGG9HkHhBGq5Q9GG98c8TrfdGYSvDvOyZ5vc/gJWPvrRTfogq+1Y3vlOZ5QWbs1f0IrPbg+ocQEVHrweZ5RNRmuJrnaX9w6/1cfvSiIfj0p5PI75Xpc6kpQD/wz2yfgGvG98Suk1WY0r9Tc4cc15ozx16SfDcRo5bz977uLKr0vYEf3gLaUIlG8zxffwPk4QghYJBcAfr6A6W626uDb3W23d/n3tsJGfX+fDUdJCKi1o2BPRG1GXah3xVf7/dyh/YJOPrU3ID26617e4LJgOfacFm+Msc+iFjDKEmwKiXJjOxDwTMg9P2+XvDnDS16PvfDrQ52QyFKTfG9cs2xFx6vc/OxcuT1zNTc1qQqxVdXzsurCXh7fV4z9qqd2FiKT0TUZrEUn4jaDDlj716K39IAUh03McvsIgc8wSx/ZlAFLywrDg33z2SkA+NQZ9hjrau/fOJEb3m7+a99i/c3Htfcpi7F33S0TLmsZOy9/D3y9j6qG33auAQFEVGbxcCeiNoMebm7IYZjGC3tV26PtQxgaxNMrDEgO1W5bGH2MSQiPcfe/XjHw5z+lpC79G8+VqZ7/4P/2qG5rj5h1TElQbmsNM8LshRfHdhX+Wg6SERErRsDeyJqM7pkZyuXnzH/Rbnc0rijdYctzdec5e6SzPzPUqhFO2Pf2gP7bw6eBQAcPau/hrx7eb66FL+uyaZc9r/cnf7zq+fpnyiv9zNaIiJqrfgLiojajHMnz8RPXX4OAEiTXD/CW1za27rjlmZr6XJ3FBoeM+yD/LzPfP4rnKkKfH1096kXjVably1bh92nqoLa/sMfC5XLy7cU4dZ3NkEIoSx95+3oGA36P9nUlS3tE303+iQiotaLgT0RtRmSwYAec+4GAJhhDd1+GdnrcmXsA4/seRIg9NwDefnq9hMVKKrwn+E9WFyDZ1bvC/wJ3Y5hqBu1m2Js7kx9U3AnLtyrUv67+wzK6yx+u+Ib3e64ZXJvANpS/B+OlAc1FiIiaj0Y2BNRm2IwmgFoA/uWlgq38krjZuMy9rHB/fMpQcLxs3W46OVvMPGpLwPah7p8PNqisdydL706JSuXAznpoNdkT91R39vfI3Up/q8K+mJUD0e3ffUSd1/sOYMNXpbZIyKi1o2BPRG1KZIpEQBggivLFsrA3MZ1pBXNmWPPdy/03APFN745gqv//r1yvbLOf8O1/WeqA36+cB/DWKvqWHTREOXyvNHd/G4vdF6Aowzfdym+esWI9okmpVeCxe1MwdcHSvyOgYiIWh8G9kTUphjNji7UiZIV/n5IB0r9+APFNS3cW+shB5R6gQxFjt7nu7DMVYL/8toDfvex93Q1KgPsuN7WDnd2WhIAR4l9INUE8sm/3A7tlNvsdgRdii9PsahpDN20IiIiil8M7ImoTTGaXMtLyVn7lpfix1ZpcKwJpoiBJwFCz9/n+3RVY0D7mfrM2pgIIoPp2RAJJmdTu0CrdeQmeQmq2nqr3e5qnue1FN91u0GSlONaEUDFBRERtX4M7ImoTZEz9oArsG9xU3zG9brkACW2wrC2x9/n031eeK+OybrbVdRZsOWY/+Zs4Q687QK4YkwuACDBFP2fMXLAbbULr9UKxapVBeT4P8Hk6mBvtQm/GXv1CRqDBPTNaq+7HU+OERG1TdH/LyIRUQSpM/YJIeqMz7hen/y+MNCILn+BfY8O2kC+e6Z+YA8A+077n2vvfriTE0K7BJsQwC+cHeHbh3jfzSGfGBHCe9b+/R+OK5flbdQnJax2u2sdey9/Ueot2r4gnVITWzJsIiJqZRjYE1GbYlAF9tuTbsG/Eh6CQbRsnW1m7PWxeV5s8DdVxD2w91VS7u+zvnL7KZzr1mn/ivxc3w8KkoBwnTQK6Z6bx2R0vSlWL+9drWoKg6sU3/U4i821jr23afrqDL1BkmD2sq49pwYREbVNDOyJqG2RJOwyD1OujjQcQmrDyRbtkglpferl7ux2gf/tPoOSatd87vUHSnAgiG7r1Dz++rl9ua9YU1Vh9/GB9vdZf/+HYx63hXrdeSFU0zxi4LtnUgXY7h3qZeqb7c7g36yeY28TylkKb3F5apJZ+7xGBvBEROTCwJ6I2pxB938F690HcFo41oFOtLYsuOQKd/pcc+wF3vvhOG55ZxOu+qtjmbWdRZW49u8/4NJXv9U8JhYCtdbGW2m3bOX2U1i964xy3Vdg7+s+QBvkyhqt+sHu1/dOw4JxPXzuzxulGiQGPjDqpnbeqh3smhMnjv/vluHqim9RNc/z1uxQXbpvkKSQnzAhIqL4Zor2AIiIIs1gNMKQ1hnlIgU5Ujmm7VgIHH2i2fsb2mTFfxIa8HfrHCyzTwnhSOObvO623Q48smInAOCgcznAA8WOkymx0GW9tQsk/ttwsAQ/G5oDwPeJKvV9/9pahGVbTuBPPx+OLumOIDUl0fNnhXup/8jcDMwc1Bk9OiYjvZ3ZY3t/hFCV4kcgrs9O8z2XXR1gr9p5WncbdWBvc16+bEwuPt58AgDwp8/3KsvmeaPO8EuS48SZ0SAF3I2fiIhaNwb2RNRmHTXkYhAKkVpfBNQXNXs/yQAGG4CbTKuwrImBvUyOd2xCIMFkQIPFlbn1lkVWhyg9vXRnpyAFENirY0NfgaI6QP3tP7YBAF5dewiPXzIUgOtkjlrPjtru7S9cMRK9Ozlua07WWVOKH/Sjg3f1uJ4+79d7ze7U76lcip/WzoSxvTvghyNl+P5wGeYO6wLA+xx59fJ4clbfpBPYF5bVobiqAZ39nChw12CxoaLOgoxkM46ercWA7FTO1yciiiMM7ImozZpy78coPvgdOqe07E/h0V3fo9eWp5Tl88jBKMkZe6FclvmLFx6+YDCuGtu8Mm3S8reOPaDNfPueY+9538mKetdjdU4KmAwS/n79GPzi7U0AoPksBDI2jzHAddIoEqX4/jL2APCn+cOxcNl2r/fbdXoYGCXH+zLs0f8CACrrHevReztPoC7Fl982s9HgMdVh1c7T+HJvMb7//Qxktk9AoK78f99jW2EFuqQn4VRlAx65cDBunNg74McTEVF0MbAnojarffsUtB8xq8X7qTrlCGwY2GvJmUybEDAZDUAQ70/PjsloFwNLmbUGgYXOoWmep5ftNxokjO3ZAZP6dUJGshndM11zy5udsUdkMva9O7XHpaO6+91u7vAueGLVHlTUWXTvV78v8mVJkpCaZEb7BCNqm2zYdbLScbuX50jUBPbOjL1OAz2jQUKj1Y6iinolsP/Hj8chSRIuH+N9hYJthRUAgFOVDQCAT7YWMbAnIoojbJ5HRNRCdoMjADVCv0lYWyVnZm124RHAqUt8NVnXGGiG1toEkhW3qz66Xhq7O7bTOTzq3cvzxzuluDLFJoOE9okmLL15HF5eMFpTum50C0wDWfNeQKia5/ndPGi5HRwnHpb/6lysvadAkyn3pn2iCe/fPN7r/er3VB6z3HSve6Zjykm586SAtyx7erKrH0Gm87K6WWGfrPY4+tRcdM1wlODLmfyqBgvuW7YDC/+5HdUN+iceiIgo/jFjT0TUQgKOYMQkMWOvJgcudiE0mUW7XWiyknYBuCceObU3dAJ5LzWl4gHOsVc9g8dj1Y3efM1BV5flzx3WBbdP64fzX1rvc6zqk0T+uvS3RLAfQaOP16nXPE/e/JWrR2PdvmIAQGZyAuYM7aK7j7QkMz64ZTxOVdbjvMGORofq90J+L+W5+PLSe02qUv3LXv8On/+WfUCIiFojBvZERC1kk5ix16Puit8pJRFnqhxr2Nc0aTvh24WAMUKl1W1RIA3Q5Pf9wJlq7DvjfflHvTnte09XKZfloFUd2Psqt1cHw5fn52Jw1zS/Y503ursSKIfj89LccwW+ZhVoAnu7dlm7fp1T0K9zSkDPMaFvR8311CQT5Ldf3l+CyfH3SA7o1RUbe09Xo9FqQ6LJf2XE9hOVOF3ZgJz04JrwERFRdES1FH/FihWYM2cOZsyYgUmTJmH06NH44IMPNNscO3YM8+fPR35+PqZMmYJZs2Zhx44dmm0yMjJQUFCg+ffpp59qttm9ezcKCgowZcoUjBkzBsuXLw/76yOitsHOwF6XUoovhCbQs9uFJousl3X1t/Y6BS6QjL18CGa98LXP7fSS+SfKXc3z5KBVXb7uK5OtDvoDKcP/311T0DWjnStYDUNkL78XwXaEV1cmuC/7p55j716K3xLqJfLk55ffezmwd+97IDfpc9cto53HbYv/s6fFYyQiosiIasb+tddew4IFC3DdddcBAD777DNcfPHFGDJkCIYPHw4AuOaaa5CdnY2NGzfCYDBgyZIlOP/883Hw4EEkJjo61Y4cORLr1q3z+jzV1dU477zz8PTTT+Pqq6/G/v37kZeXh+7du2Ps2LFhf51E1LrZnedIjWyepyHH8na70MRfNrvQBO52nfnHFDoBdcUPMEL2V/pu183Ye88hGIMM7OXg1RXXh/YD89Y3R1Ck6vIfDPX77B6z65fitzyw75zq6tgvv+WJzgtNNv3A/v2Nx/Hbmf099iVXY1wxJhf/2FQIACivbWrxGImIKDKimrFfvHgxFixYoFwvKCiAEAKHDx9Wbtu2bRumT58Og/OHwaxZs3DixAns3r074Od58803Ybfblefq378/5syZg6effjpEr4SI2jK7PMeegb2GnPG02gV+cnbcBpyBvZ+MPRP2oRPIWxnoCRV/m5XXOrLBgWbsm2yuPXZK8b+snEw+MeSjHUCzPPqZ67dFsB9B9cucO7yr5j45uC4sq3OV4ofgF9jw7unK5T6dHOX8ZpNjIEdKa2Gx2fHtoVLNY348Wqa7L/mtvGZ8T7x01SjNuImIKPZFNWOfl5enXLZYLHj22WcxePBgzJw5U7l9/vz5WL58OW644QYkJyfjvffeg8FgQKdOnQJ+njVr1iAvL09TVpefn4/FixeH5oUQUZvGOfb65FL8baqgHnBlLGXqwD7UGVgKtBQ/NBn73accE77P1jQqt/kK7NXPm97O7HU7mbwnV1f8MDbPCzKyV2fg+2a1xw8PzMDnO0/j4RW7YLMDZ6oaMPlPa5VtfFUyBOr6c3thfN+OaLTYlf4ESc7588+s3oeiinq8v/G45jHegnXXFATtNBoiIooPMbHc3e23346srCx88cUXWL16NVJSXE1k/v73v6Nnz57o1q0bevbsieeeew5PPvkkcnNda7GePn0aV1xxBaZMmYKZM2fi9ddfh11V23n48GFkZ2drnjMnJweVlZUoK/M8c93Y2IiqqirNPyIibzjHXp+3gM5qc+uKr/O2MWEfOu4Bp55Aw7dA4zz1vPtA55Kry/e9kU/Qh3GKfbO5d//vnJqkBNl2IZR16mUdvCxrFwxJkjAwJw0jcjOU9++aCT2V+5dtPuHxGK+BvfPdlCTtNBqKH0dLa/Hgv3agsKwu2kMhoiiIicD+lVdeQWlpKQoKCjBx4kScOnVKue+GG27A8ePHUVhYiKNHj+Ldd9/FwIEDNY/v168fnnjiCXz99df4y1/+gqeffhr33Xefcn9dXZ0yH18mX6+r8/zj9+STTyI9PV35pz6JQETkzhXYsxRfzdsyZ3ahX4rfZLVzjn0YqAPmQyW1GJmb4bFNoPFbcwI9X13x1RUAgZwAkDeRS/HD+XkJdt9GnRS/fJNdCKQluSoSJvTp6LFtqEwb0BmzBjuSGY1Wz7Nm3gJ7+WYJknIyyKb6blLsu/aNjVj6/XHc+NaP0R4KEUVBTAT2AGAymfD444/Dbrfj+eefB+CYX7906VI8+uijSElJgSRJuOiii7BgwQJs2LBBeey///1v9O3bFwDQt29f3HPPPXjhhRdQX+/IGCQnJ6OxsVHzfPL15ORkj7H8/ve/R2VlpfKvsLAwLK+ZiFoHmzLHnj9+1fQCHUAOLLRrkZfVNmH4otXYdZIVUqHmHjDrxc+upneOOx+/ZChmD8nW2a7lz68WbPCcZHZ819QfrVCW43dUZdFrGq0+tvSkfplyZYH82m12gfaqTvn7fSwpGApPXDoMf5o/HE9cOgyvX5OnuS+gUnxlqUqBd787iv4PrsLafcVhHTO1XGGZ43fvweKaKI+EiKIhqoF9U5O226rBYED//v2Vxnj79+8HAPTq1UvZJjExETk5OVi2bJnX/fbt2xc2mw3Hjh0DAPTp0wdnzpzRbHP69Gmkp6ejQ4cOHo9PTExEWlqa5h8RkTc2559SgyRgRnDBQGvmbQqxI7BwBRd2AazYVoQGi+vESLBLjVHgdANt5+GQs8r5vTIxQpXZz0g2OzcLLIge3j0dnVISMSI3A118rIMe7ImCRGdTPvUrCGXWXj5xAABVXpaF80avQkV+r9cfKEWt6kTB2TB3m89KTcTl+blYMK4HfjY0R3PMrV7fdFcpvvxabELgoRW7AAB3vL81rGMmIqKWiWpgP3r0aI/bTp06ha5dHd1ku3Xrptwms9lsKC4uVjLta9as8VizvqioCJIkoXv37gCAGTNmYMuWLZqz+ps2bdI06SMiaq6cDhnK5UmGHdEbSIzxmrEXQhOM2YUIyZreFJh6i+eUETlglw+LBAmdU10BeX4vx0nwQIPonw3NwaYHZ2LF7RNh8jF3Xu9EwQ3n9vK6vRx4q/sGhLIaXz01oGMQXfoBtzE596Nez/4fP0av+u/SUd2Uy34z9pBczfNURUgWGyuSiIhiWVQD+927d2PlypXK9aVLl2Lfvn24/vrrAQBjx47FkCFD8NxzzynN8F599VXU19dj3rx5AIDCwkI888wzylz5srIyLFmyBNddd53ShO/GG2+EJEn48MMPAQAHDhzAqlWrsHDhwoi9ViJqvUb0df1oTgWbFsl8Nc+zuwX27mt6M8wPH70mdXIDQzkglSTg/GE5uHPGOXj4gsEYlJPq2M5LZO9eDj9vVPeAxqK3O18neRKcY29OKf6Tq/bg2r9vhFUVoP7jx+O46OUNKK5qAOB6fVeMyUV+r8yA9iszaMbk+P9J57hW8CmL4prw988ZiHG9HSdnvGXs5VsNkqsvgrqnApe+i30sdCJq26Ia2C9ZsgSLFy/GpEmTcO655+K1117Dp59+ikmTJgEAzGYzVq5cCZvNhnHjxmHChAl4//33sWLFCmWpvBkzZmDUqFGYNm0apk6dilmzZmHu3Ll49dVXledJTU3F6tWr8frrr2Py5Mm46qqr8NZbb2Hs2LFRed1E1PqsFyMBcC17NV/N84RbKT4z9pEztGu6x23y8ZBjN4MEJCeYcNes/rhpUm/lWHoL7G12oQmwTcbmH88r8703rJXHISH4jP1fvjqM9QdK8fWBEuW2+5btwPYTlXhq1V4Artd/w8ReQU8HUX/e5fcz0WTELZN7A9A2slt00ZCg9t1SnVIScc/sAQB8Nc/zLMW3qpas8F7CT7EiVVUhUqmaSlJe24SNh89ylQOiVi6q69jfcccduOOOO3xu07NnT3z00Ude78/NzcVLL73k97mGDBmCr776KugxEhEFwu48T2qUWK4qS/BSgm2zu2Xs7cKjoRszT+Ejz1NXk2NyV3DuXkEhB/b6+7QLQFLd520ahufzeu7wnOxU/w/UyY4HSt3LQVbVYNGMx72CJBDqx6jfpwTn+y13lk80GXC9j+kG4aJ0uvdTig9IruZ5jAPjSrfMZFSdcjQgvefjn/DX68YAAG5860dsK6zAc5eNwPy8wKppiCj+xExXfCKieGaTHH9OmbF3GdRFv/Goe3ZXrxSfwkfdIE4m3P7f/XDIJ168lb3bhdAEjN6qNTyeN8DAsWt6Eh6/2JXl1lsuMVB6ge0+Z5d6dcVCsIyawN71HAlGx/vdaHX8bQjF+vXNYTL4C+xVGXs/JwEoNvXrnKJcPn62Du98dxR3f7QN2worAABf7Dnj5ZFE1BowsCciCgE75LXsmbGXGQ0SenTwXFLUEdi7rrMUP7L0M/Zyyt7xf+5HQw7UvcXQQgS/Jj0AnNuvk/+NAPzz/87FtRN6ucbTghNBeicC5GXCXOXowe9f8lJFIGfs5VL8aH3SjTrl9TKbXaCqwdG13yBJmmX6KH6oS+1rGq14eMUuLN9SpNwmf0ZrGq2aXhNE1DowsCciCgGbxMBej0knwDtcWus2x16veR4D/XBJNPsoxXdedw9s5avesuMvfrFfc1+g52nyembik1+dix8f8L1Kjfuc/ZYsd+crWJUDo2Zl7FUPUgdYZufYlcA+StUp8nuo9/pvfOtH5bIEV/VBsNUQFF3qY1ta06i7zanKeuT/8Qss+NvGSA2LiCKEgT0RUQjYnBl7luJr6WVuV2wrgjppKIQIuHSbWi69ndnjtjV7iwGoyrHd7jcogR4028n+8vVhbSl+EMHrqB6ZyEr1vbScyaD9uaLJjge54J2vJnDyy2rpHHv1M8gVEkdKa4PeZyjJJ9lKa5qw+VgZ9p+pxstfHkBZbRO+3u9qKOhonue4bLMLpCW52jGFq/nav7efxMrtp/xvSD6t21+sXFY3a5T9VFiJOz/YhnqLDT8cKcPilbvx7cHSSA6RiMIoqs3ziIhaC1cpPgN7Nb3A/vvDZZg+sLNy3Wb3bLbGKfehNXNQZ3yxpxiDuqShv5fmdMXVDX7n2MsZXL34Tn2ypqVTKwbmpGLv6WrlumfGXr1mfHD71usTIK83b29R8zzXZXWmOyM5OnPq3aUmuU7o3PvP7ZAAHCqpxWnnUn8ySdM8T6Bf5xRsOV4BALAJAUOIq2kq6yz49ftbAQAT+82KmfcrHuk1hlQrqqhHUUW9cv2v64/gn5tPYMtDs6JWSUJEocOMPRFRCNidzfOMQWYPWztvAV5xlatM1GYX8NJAn0LkuctG4uELBuPtm/K9Bq01DVYlSHafCiFfl+/XK9E+U+0KEAPtiu/NywtGa66bfWTsg2+e53lbk/NG+YRFc4YvaZrnuW6fNTgb+b0yVdsFv+9QyE5Lwv8V9AUAVNVbcajEUUHwlSpbDzjGJx+/0pomJagHAKst9H/fapusyuXyOouPLSlQmclm/HJqH1w+pjum9s/CteN7YkKfjujV0bPnSXmdBSfK63X24iKEwM1vb8Jt724O15CJKASYsSciCgFm7PWp59iPyM3AT87uzP/d7erObLMLzzndERld25GebMZNkxzrqZ+p1J97axdCKWt3Dz7l60LJ2HsGeD8eLVMut3RqRb/OKbh7Vn88/7/9GJCdiiSzr1L84Nh0msdZlMBe//UHakRuBnYVVWKSqilgktmI26b2xY9HN7Vo36Ewf3Q3vLbukOY9UGd5O6UkICs10evJEkfjPc9VFVpCPYWjttHqY0sK1IxB2fj9nEEety/85084erYOADCqRwa2Ok/aHC+rQ65Oo1NZaU2T0lG/ss6C9GTP6TxEFH0M7ImIQkBunmeSGNirqTP2c4floL7Jiv1nanC8rE653Wq3tzjDS4Hz9lbbhfeyds859p7bWJxzekO1wsFvZpyDy8fkomNKgs6Jn+aX4us1jxNur6u5XfeX3TYBNY1Wj3Jy9XsSzcaQRmflg7rPQKPF9Tdrw33TleUQ+3VOwcHiGs3jw9El38rAPuS8HafrJvTCR5tOAHCcdB2QnYp9Z6r9Hlf110FvVQUiig0sfiQiCgEud6dPHdAYJAkPzB3ssY2jFJ+BfaR4C1ptdhHEHHvPQMDago7y3uSkJ8GsM09DMz63oSzfcgJ3/2MbmnSahwG+m+e1ZI49AJiMBt054urmf9E8hyVX0KhL6uVD2c5sVIJ6AEhN8sz9+HrvmktdPSCX5TdZ7bjrH9vw89e+bXVN9d797iguez30r0vdO8LiZSk79XfJIEkwm5wrJfg5O6Y+8epvWyKKHmbsiYhCQJ5jn41y4PSOKI8mdvS2HcEBGFGBVEiShKn9szC6R4Zm3m5FnQVm97XVGeeHjcHLKX2bXbjWsXeLPt3XsdfL8MlduFuyxnyg1M/gfpLh7o9+AuAoi7/+3F4ej/UVl7gC+5aOUEvd/C+aH229Je+qG+X167XbJuicUGnJHHu7XaC8rgkdU7QrIKhPFuw/U4OctHY4XlaHT7Y61l9vsNowd3iXZj9vrHloxS4Ajg71MwZ11pxMcVfVYIFRktA+Mbif696Ok/oEqskoKRUc/lY70PS04LlropjFwJ6IKASSEpMAK3C56Svg9UnRHk7M+BOARxMTMalxiRI4JLgF8Te/swl/u26M5jb+eAwfb4G3Zo69232SahvH/3s+vqTaMXc/Ehlpb0vLqamne6j5yjjavZzYaCl1r4lodh+XAzu9cmr3cbl/T709LlB3fLgVK7efwrL/Oxd5PV3NBNUnGZ5atRdPrdqreVxtY+uc3tRks2Peq9/iP3dO1r2/ttGKyU+vRZLZgA33TdetXFFTf6zde1LITG4VVPL5Jn+VGOp9sxSfKHYxsCciCoEBBVeicNW36GRuQjsfGZi2xlZTjGSpEX2lk0owlmDyfH/Kaps019Wdsim0vGWjbXbh6orv0TzPtfwZoL9kXFWDo6O5vyW3QkHTPM9LoF5Vr99h/WyNfvNA9X5CnbHXzrGPHnlKgF4c516FkagT2Ldkjr1cev7aukP42/WuE3nqfXZOTURZbVObmXe/+1QVhPBsHgoAp6saUFlvQWU98MORMkxUNWTUoz4yv55+ju426s+h0SC5Pg/+AnvV5XD0WSCi0GBgT0QUAgPzZwL5u6M9jJhz7Ilx6NO0F+lSrStjr5N5ci+nDseyWuTgHkT07JiMY2frnBl75zZu4ad8Usa13J3nfi3OY9Y/OyWk49UjBZCxr/ESEBZXe1sVwHU51NMJ1HPsoxnZ++plUW/RZsb1MsShmGNf3aA94SLvs0eHZHy9cBoueeUbbHOungEA9U2tM2Mva7Tadcvx1cH2J1uL/Af2qr+hHdt79nkAtMfUZJCUaTn+M/au+xnYE8UuBvZERBQ2dcZUAMAc40bUSdcD0M8Euv9WbLS27h/z0eQe2hmVbLzrB7z35nny/+s0z7NFbo69mrfKem+3e5uvbNdk7EP7GmKlOaTZGPg4dEvxQ3DCbeORMvS6fyUSTQY8f/lIdE5zzLmX3yP3561utKLX/SsBAGN7dUBaOxMq6iz46JcTWrysYiyob7LpBvbqYPufm0/g6fnDfX6O1EfG28fXvZmpfN3b8oZ6+w5HA0UiCg12xSciorCR7I4SezNsXufYA54/LBu9dDSn0Dp/WI4SBNjsQjXHXLudK2MvIITAun0lHvtqCvFyd/7IT+OtFF94yeV72179GZRC/OvIHCPN84I5NuN6d4QkAamqEyF7T1eFbCyNVju+3FusZIDlsemd+JP9cLQMX+wpxqZj5dh3pjpkY4mmv64/jJXbTyknxmTumfHTVQ0+96P+WHtbUtFjjr0zZe8vC6+ZY89qKqKYxcCeiIjCpnbgZQCANNQpwaFedso92Gq0MGMfLk2qAOLZy0a4snaqH/fugYGkZOwF1u0vwT0f/+R1v5HK2Mvl+MGGGeqPmjrgVt8ezox9NJvnmbwtiaBjwbge+OmR8/DjgzORk5YEALj3n9ub/dzDuqUDcHzm7vvZQABAvcWqBJVy0KmeqjMyN8Pr/hpayd+IV9cdwu3vb8Gqnac1t7sH2zY/AbXmRJa3jL368w4RePM81b7ZPI8odjGwJyKisDElOeZbJ0sNSob1pom9PLZz/xHbwIx92Kib2yWZjEoQq+4W7715HvD9obO6+5XXzo5UebT8LMGW4qsz8+o5x/YwNs/TrGMf2l0HJdjXlZZkRpLZiNum9gHQsrHL3/FOKQnolOKYA17XZFOCSvnkh/qYZCabve4vEk0aI2nRZ7txwZ/X47+7HAG+e7DtL6DWZOy9HCh1xt4uoHtST3/nrosWZuyJYhYDeyIiChtTkmOOfXs0KMHhOdmpHpk499+V0wZ0jsTw2qTeHdsrlw0GSbUEmjpjr6WUvcP7NAn5B38Q07hbRFLG1PxAQxvYu24PdcbebFJn7EO666BIkqQJ7gJ1vnMd+Sab3etUBn/kEydGg2td9jNVjdjnLO/Xm2OfnOC9FdRPJyqw6WgZKusczfgKy+pwsDh+y/NLaxqxs6gK735/DIBOxj6Iue3ejrB7xYbed18Pu+ITxQc2zyMiorAxtXNk7NuhURMsLb15HIY+slq5vv6Aa872f++agv7ZqZEbZBuTnmzG97+foSzLKP+4V5f6upeLq+fYv/XtUd39WiJdig8JgPCamffGWym+3UfFQkupA9RoNx8zGaWgxyCXxwvhCOxMzTh7I7+9BklCcoLjs7fnVBX2nHIE9iadOfa+mv3J691npyXi7ZvG4mcvrgcA/P36MZgxKDvo8UXTitsn4su9xViy5oAyxcA9gA4mU+5tuof6pI63z74e7Rz71lUpQdSaMLAnIqKwSXAG9jlSOcZuvR84lgwASAHwTf96bDxShh323nhz3xwAwOAuaQzqIyAnPUm5LAcBvjL26jn23sjN8yJWiu9nTN5GatcE9q4gUt0ULNQnJ+RAFgAaorx8myNrG1xwps6iW2wCJs82GX7Jx0mSgPxeHTCxX0cUldcDcHxmrh7XEwBw6ehu2FFUCSGA+XndcU52Kv65+QSsdjsKy+qV/eWkJeF0VQPOVDVi+4lK5fYdRZUxG9h7q3YYkZuBslpHo1F5ikGwGftATnAZDBJumdwb6w+U4prxPfHBD4WafX+8qRClNU24bWoftyUlXTu3MGNPFLMY2BMRUdhkZOXCLiQkS43oceIz4ITrvm4A5hmBecYN+LdtPEqQGdUy5bbKqBMge+uK72uarxLYR7oUP8g59uogRR3Yq5dYDHkpvup56qLc9K05qxaox99ktaNdQvCRvfz5MkiOUvz3bh6vu925fTvh899OUa5PPicLt0/rBwDIe/x/OOsMgL9aWIBhj/4XTVY7quotyvbF1Y1Bjy1SfAXfcqWC/Dl0n1MvX2+02pCoc2ZF/bn2dYQfmDtYufzRj67AvslqV5ojntu3I0aopkupx21j8zyimMU59kREFDaduvbErulvYOfQhbDN+iNw3mLNv3rhaKKVKjkycQzsI09/jr1+V3ybj8jkcGmtZn/h5m1JLxdvy925LqtLytXN2ML5EqK9XFhz5tibDJLyGWhqZim2uhS/ubp3SFYuq0v6K+pcgf0nW4qavf9w81Xxkmh2/CT3lbFfsa0IQx5ejTs/3Orx+ECa57mTq2ueWb0P9aoTThe/8o3mRJd6JGyeRxS7mLEnIqKwGjZ1ntf7alY/jXZoghnWCI6I1ORAq6KuyXWjW2BgCnC9a/X+ws1fxj4QRtVYLaqANRxL0s0b1Q3Ltxbh6nE9Qr7vYDRnfrwkSTAbDWiy2psd2Lsy9s16OAAgN7MdfiqscO5HQrLZiApYcKikRtnGVyf9aPP19ZGz8OW1Tfjb+sOaKgnAEVC/v/E4rHaBFdtOYsmVozT3q3ft/6SXQ9f0dgCA1CSTxxKju09WYVSPTMe+VV+ydftKUFlnwUUju+ouXUpE0cPAnoiIoqYJjh/hCc7APtAfpBQ68tKCPxwpU25zj2uTlGyi/zLySAX2yvSAICN74WXKgXzSIlzZ+uevGIkn5w/TLaOOpOb2PktwBvaWZi5FKQe1LTlpoq42MEiufanXgK9UleXHGvVndVSPDGw9XoGp/bMAAOntHH8Lqxut+OPKPR6PtdmFzxNr3j7Xvlw+Jhcvrz2Iuiabx/KBm46WqwJ71+0f/HAcH/xwHGdrm/B/BX0DeyIiigiW4hMRUdQ0Ccf55QTE7o/x1q6DM8OZmuQ61+8eF8jBqLel7tQiV4rv4C3U8b6Ovf42rsA+fOOPdlAPeG/g5o/cQM/SzDMDoThxYlJlsSVJwoS+HT22qW2yKUvgxRr1W//EpcNwy+TeeOziIQCA3A7JeHDuIEwbkKX7WL/r2DdjPHKvhLomm6YUH9BOu9H7yHx/+GwznpGIwomBPRERRY2SsZecGXsm7CNuePcMANq5s+5ZVX8Z+8Fd0pTLkWqeB6UUP8iMvZfbrREI7GPBxH6dlMvnD8vRLC/ni7w8WiAnd/QI0fL31335u+kDO+tut7WwvNnPEU7qjH2vju3xwNzB6NmxvXLbzZP74IUrRqK9TnNCq034DN6bM8devVqDeslRQPtd9/3MRBQrWIpPRERRY4GcsZdL8SnSlHXsfSx35y9jP7hrGnY71yOP3Dr2DsGGHOrgSv1Yudt3K4/rNXPQfzhSFvDrled8NzdjL3+8WlLR4f7YZC/d+Zt78iHcfK08IctITsC6e6ehsLwOZoMBD/5rB346UYldJ6t871wd2Af4l7Sd2QiD5Dg27uX/6vewJX0siChymLEnIqKoaXIG9pMMO6I8krZLDpa0zeO02/jL2KszqRErxXcO0vuydv6ps/3yy2/tGfuf5+Uql0trmgIOAuVS/BPl9X621Kdex765ymu1JfbJCfr5qUB6QTTX5ztP4+nP98LejPXc1Q/x9TnLSk3E6B6ZGNY9HWXOppbJCUbNkfL1/MF0xf/NjHMwuEsaBndJw9BuaejY3rFSiTZjT0TxgIE9ERFFTTs4frTmG/Y6bmjlQVUs0s/Ya49DgrLGtmcmdFzvDpoO3pFrnuf4f2+l+EIzR1g9sV73ojKHubV/BId1T2/W46rqHVU1e075yRx7IX+8WvL5mOYsvR/bqwMAoEt6ku52wWTsLTY7qhsCn5N/29LNeG3dIfxvz5mAHyNTfw4DPf81yTl1wv1khdUtsG9uufxvZ/bHf+6cjP/cORn/vmMyrpvQy/l86ow9Q3uieMDAnoiIosYy5lbnJUn1vxRJ8pJv2jn22m3kwF2vK/ffrh+jDewjnbEP8nF2L0G+vY1k7N0Fergm9nM0qovmHPuLR3bF/7s2Dy8vcCz1ltshGR/cMh5PXDoML14xEuc6m+lZA1xrvcFiQ8Ez6zDqsf/hq/0l/h+gUlzVENzgEXjGXk09DUY7dcQtsNeU4jefXJ3z312ulQb03s029jUhigucY09ERFGT2rknANcce4o8OWPvq+u2XlYfAHp3ao/UJLNmbfRINc9T5tgHUIqvieW9bN9WMvbuAl1+rn92KgDg7xuOYEr/LGWZtkCFYh17s9GA84bkaG6b0Lej0h3/c+eydzbVZ/mr/SXYcszRTC85wYjLx+Qi01luXlRRj6IKx9SCzcfKPV5TZb0FH/1YiJpGx9+nDFV/guYIZI69u0Qv02Acn1dXjwHNOvYt+BCnOZfdMxokvPC//QCAcud0ALV1+0rw9f4STAnyc0BE4cPAnoiIokYyJQJwLXfX1oKqWKAXtLtnE80G/QI/OUhLS3IFPO0TI/PTQh5iICXImiBfdU0vA9rWMvaBvtzOqYnK5Vve2YTtj5yHJHPgy/eFYh17f+QTTHL1SYPFhlve2YQmVZVBbaMVd583AAA0t9c3eZ5cXPr9MTyzel/IxqfuMxDo+6DO2Ksf4Zmx9978MhiTz3GU/hdXN2LJmgM+t73lnU34KcjPARGFDwN7IiKKGo/APpqDaaMMATTPMxr1j4x8UuDK/FzUNFphtdmVObrh5q95njerd7nmRquDIfn1myK2Xl9sUE+j8GXu8C4oLKvDS18eRJPVjromW5CBfcsz9v7Ir0Wuvmi02JXgfVi3dOwoqlSa0QHaz3xdk2fDvZLqRuWxAgI7i1z9BVbuOIVrg/ysyx+3YE4eyaXx735/DKN7ZCi3W9ymG2gz9kENS6N7ZjKemjdMtwv/8O7p+OlEBb49eBaHS2vRaLWjPsjPARGFDwN7IiKKGoPZGdhLLMWPFlMAy915C3blAKVjSiLu+9nAsIzPG3lEdq/N89SXtdvY7QIGg6QJhpqcgZLJy0mM1uTJecPw++U7sPBnA/DOt8cCekxyggl3nzcAf157EEIA1gCXvatusOBIaa3SxT2cFREmgzZjr/5snDc4GzuKKjVZevVlvcC+1lmC/7OhOTincwpufXezcp97YB2I5pzc6NTe8TcyyWzQnFj4Ys8ZzdKFVQ2uv6EtrYq4cmwPr/ddNsaxqkKf36+EXQBNzVz+kIhCj4E9ERFFjWcpfusPqmKNfvM87XHwtoRdNMvWlVL8Zix3ZxMCBrfTFxarnLFv/X2FrxrbAz8bkoPM9gn48IfCoB5rNhjQZLPDEuBybxe/8g0Ol9Qq18O5HKJJztjrBPZyVln9OVcH9vI8erU657z25AQjzhuSg2/vn47Pd57GY//ejar6wDvpy5ozHWFyf7krvjaA/v1y/SVCI1VxkmAyoEFVEUFE0cfAnoiIosZgcjSxSkMd3jU/gbRyM/BO85bjouaZWtWI/2duwr+absY2ON77QDP2R0prdW+PBH/rr2uWu3O7z2YXMBu1JwXkzGM4A89YIjeQk5cyDJTJKKHJFnjGXh3UA+Hto2E2ahtBugJp1+tUB6KNqtfw/eGzuOZvGzX72+1c2i85wXFSoGtGO4zt7Vhq71hZncf2/sgN8IL5iOl9Hod2S0OSSb/8febg7KDG1FwJRkdg39xVEogo9BjYExFR1KR07IJGYUaiZMFk406gCcDhaI+qbckGcJ4RqK3vhv/gSgCewZe3DGO9xbN8OVL8ZezV3LeRM7nqRnqcYx8Y93L3YCSYDEhv17LO8r7I1Rby2NRL7MmBfaPVjvLaJlQ1WHDS2REfAKobrNhwsFR3v7mZycrlrhntYDZKaLLavW7vT3ZaUsDbujeunNCnIz64dXyznjeUEkxGAFZNnwI9DRYbzrgtDZiaZIZdCGQmJ7SZE2lEkcDAnoiIoiYtoyMOX7YSZYe3wmCQMCA7NWJd1cmh8MdPkVv4GVJFpXJbPEyJkKcBBNIV353VLgd+rtvkYLCtBRrdMpKw55RnozRv3BvUBWPF7RORmhS+wF7O2NvcMvYGyTXubw+VYsziLzQ9JdqZjXhy3jDdfXZOTVSW0wOADu0T8MmvJuJgcU2zxzmmV2bA27o3rgzniZFgJDjH5asUv9Fqw7Rn1+FUZYPu/VP7Z+Htm8aGZXxEbRF/PRERUVT1GToOfYaOi/Yw2qzyY8XILfwM/S178QfTe44bV3/rsd0fTEfwtX04NthdAVBuh3aRGqZXT63aixevGIlPthahX+cUzX3Hz9Zh2ZYT+NlQ7drndp3AfleR48RGchs7sfS78wbgYHENfjGpd0Dby80Frc3I2A/qkhb0Y4Ihj+2d747hD+cPUi0v58rYy03yTAYJSWYjjAYJC382AJeM6hbw8wztlo6h3SIzZci9guSCEV0i8rz+yO/n2dpGr9uUVDcqQX2K83vVYLEpJ9a+2l8S5lEStS1t679eREREpGFI6woA6IEzuNW00nHjd57b3WoCrhJfYnjjXyHg+FH/hzmDIjVMD5XO5mXfHjqLG978UZkPrfbAv3Zg/YFS/GfHKc3t7muAA8Dnu04DAJKCnHMe7wZ1ScO6e6cFvL1c7m4NsHleJMnZ7EarHXtPVyM1yfEz1yA5svJql4zqhmcvGxHxMQZLPVVi04Mz0SklMYqjcZFPkGw9XoHpA/Xn9cvZ/NREE3Ysmg0AWPTZLrz5zVFlG5tdtLkqGaJwYWBPRETUhg0890J8f3IhRNVJAI51rHMzPTPx1g1/RqpUj3OkIvQdko+8npmYMSgyjbr0pCWZlE7mxdWeWUMhgG8OOeZAH3Arm7bpTMyXA5W5w2MjIxqrXBn7wErxe3RIxvGyOjx28ZBwDgsAcMWYHnjiP3sBAGdrmpQssUGS0DVDO69dXh8+1pmNBrxxwxjY7IiZoB4AumW2Q3F1o89VJOSGlOoGje5TCR75dCf+eIn+NAgiCg4DeyIiojbMZDZj/FUP+N/umyUAgKuMX+LGYRmOG3f9EMaR+faLtCPYUG3DV/YR0Ov/lpWaiPYJJt1lzOTp4e7r2wPAhcO7hnqorYpcGh5o48QO7RNwvKwOXdLDP20jPdmMkbkZ2FZYgbomq2rdeAl9s7TTNBK9dJWPRd4y4tE0rFs6th6v8Nlr4VCxY0UE9bdsdI9MGCRX/4Mv9xQDl4RvnERtCQN7IiIi8mtd0kwUNHyBG02rgU9WR3s4+AWAXyQAdzb9Ct/Yp3vcn2A0IDVJP7C3KV3xtWYPyVaWgSN98rmQbw+dxeRzsvxvH+bxuGuf6AjY6y02zXJ3SWYjJp/TCesPOKo44iVjH6vcVyDQc/v7WwAAZbVNym1T+mdh84OzcLi0FvNf+1aZUkNELcfAnoiIiPzqeeF92L6qAX0yjEqJczQVH9uNztZTWJLwKi60DUIpOmruFxDIzUxWmncN65aOHc4GeXrN8wAgLYwd21sL+aSIt3XUvYnULOp2Zsdnc9WO00qDO3kFhaxUVyl7PGXsY5H7CgTuXlt3yOtjM9snoI/zcm2TDev2FaNgQOdQD5GozYn+f5mJiIgo5vUeMhYYsjLaw1AcXfE6Om+9DwAwU3yLHbhQc78QriXZXr8mDz8bmoMhD3+O2iZHV251FlFmMrKJlz+Tz+mEY2eP6/Yp0BXodiEiB5qnqhpU69g77lOX4/fokOzxWAqc/F3xlrF/45sjPh+fpppr/+53xxjYE4UAA3siIiKKO2MuuAXHt7+MHrZCGIXnfG+7cHVul7OLBmeEd8s7m3TXIWd3bv+Mzuy3Xn8CX6QIvbXXndsLa/eVoK7RqlrH3vHkt07pgxHdM2A2Ssjv1SEyA2qlXKsj6GfsG5p892AwGiTcc15/PPvf/bDE4AoLRPGIgT0RERHFHYPRiOIO+ehRUgjoBPYCQskmmpzd9eTAXS+oB+Czwzc5SJJcgh1YMBbpkC3L2Tn+QHGNZh17wNFhftI5nSI8otZJPlm29PvjsAtgUr9OOH+Ya0WJRudSdzlpSbhjRj/dfeQ6qya8lfMTUXAY2BMREVF8ktPAetlj4VqSzewM6I1+0sYmZuz9kk+OBJtkjVTGPjvNtazd0VJHV3Ye1tAzqN7U9zcexz83n8B5g7NhMhpgswtlqbv/3DkZHbw0pJQ/S1YfDfiIKHA8NU1ERERxSUiOBmiSTsZ++dYipRRfztgb/ER4Rs6x90t+C+0BluJHeIq9pkFeVb1jRQRDpM4qtCFNVrvH9aoGq8d9iSbvoYZ8Ii3Q6g8i8o0ZeyIiIopPkiNokLwUfB9xZmzlzKC/jDwz9v4ZmhmMSRHriw90y2iHoop6NDqzxjysoVenmkOfkuhYVnLp98eQ2T5BM78+yex99QFlyTwG9kQhwcCeiIiI4pJwBvYGPzO5kxMcwUU7H0EGABg5x94veTpDwBn7iM+yd3VslzPHEjP2IddRVV7fMSUBNY1WPP+//Zpt2pmNPhtSGv0smUdEwWFgT0RERHFKcv6v78BgYE4qAOAP5w/C8q0n8J8dp3W3Y8beP7ms3R70JPswDMYL+TjKgT3P14Telfk9UFheh0n9OsFoMOCTrSc8pl3MHJTtcx8mL3PsS2sace/HP+GqsT1w3pCckI6bqDVjYE9ERETxKYCM/c/zuisZ25mDszFzcDZuf38LVm4/5bEt17H3zxBk87xIz7EHVCXeSik+j2uopSeb8cdLhinXZw32HcTrMXqZ1vH0qr1Yu68Ea/eVYNvDs3CivB5Duqax8oLID57DJCIiovjkJbDPTDYrl81Gz5863jLzZqZ2/ZLfOluw69iHYSzeuJfiM7CPTfIJGPfA/mxtk3L5vBe+xgV/3oC/rT8S0bERxSP+F4yIiIjikxLYa0vxJ5+TpVzW68rtPu/XZJAwIjcDMwZ1DsMgWxdjkKX4UcnYO0/myEuuMa6PTfIJGIvbHHuzqnKmuLoRALDnVFXkBkYUp1iKT0RERHFJWe7OLWOfoArmzTrl9e4Z+1V3TsY52alhGGHrI5fiW+0Cdrvwu4SgLJJl1Gb3OfaM7GOS/D20WAVqG63K7Xong0pqGiM1LKK4xcCeiIiI4pMzYDO6ZezVgX1CABl7xn2Bk4Pkf24+gb2nq7Di9kk+O59HYyEzORPcaOVyd7FM/tycrmrAkEdW+9x2/YFS/HnNAdwx45xIDI0oLrEUn4iIiOKTlzn2CUZ1xt5/YB/ZGeDxTf127iyqQkl1YJnUSL7D8jHfdbISADP2sapPpxT07Jgc8PbrD5SGcTRE8Y8ZeyIiIopPzsDefbk7bSm+XvM87W2M+wLnHiRX1luQk57kdXsRhUn27RMcP2+3n3AE9klmY8THQP61SzBi7e8KlF4IapLkKsnfcKAUN7+zCfUWW4RHSBRfGNgTERFRfAogYx9I8zwKnHtgP/vFr/HJr87FqB6ZPh8XyZMnv5lxDjKSzWiy2WGQJFw+JjdyT05BMRgkJBl8n3hJSXKEK3VNVp/bEbV1DOyJiIgoPnkL7FXBvF4ZdqlbIy6G+YHTOyny2U+n/Ab2kTS4axqemj882sOgEGnnrLhosHhm9onIhXPsiYiIKD45u+IbJO+BvZ5xvTtqd8Na/IDpFTv4yqTK5dQST59QM7VLcHzPWYpP5BsDeyIiIopPzoDcfY59SqKrILG8rsnjYUlmtzn2YRhaa6W3vF1tk/+Ai+dOqLnkjH19AJ8zoraMgT0RERHFJy+l+AZJwjmdUwAAI3MzPB6WaNLO6WXQGbislESP2yrqmlBc3YBGq2fgJaKy4B21JnLzw3qLLSrNGIniBefYExERUXxydrd3X8dekoC3bhqLvaeqMG1AZ4+HuWfsKXAzBmUjwWjQdDJff6AUYxevQaeURHx5z1SkJZk9HsdzJ9Rccik+ABw7W4dendpHcTREsYv/ZSMiIqK4JHnN2APdMtphxqBs3dJxj4w9w86AGQ0SvrxnKrpltMMN5/ZCTlqSUvFQWtOIIyW1mu2ZYKWWaq8K7PefqY7iSIhiGzP2REREFJeEl3Xs/QXqGcnajDJL8YPTPTMZ39w/HQDw6EVDAAAFz6zF0bN1umuSA2DKnppNkiRMPqcT1h8oRVUDl7wj8oYZeyIiIopPcld893ncfoLIIV3TwjSgtstsdPyktFhdgf2yzSdwoLgmWkOiViS9neNk3D0f/4QPfzge5dEQxaaoBvYrVqzAnDlzMGPGDEyaNAmjR4/GBx98oNnm2LFjmD9/PvLz8zFlyhTMmjULO3bs0GxTWVmJa6+9FmPHjsXo0aOxaNEij+Yau3fvRkFBAaZMmYIxY8Zg+fLlYX99REREFEZeSvH9JYclScKDcwcBADq0T0BWqmdDOAqOvMRgozNjf7amEb/7+Cflfk53oJZQN8H85+YT0RsIUQyLain+a6+9hgULFuC6664DAHz22We4+OKLMWTIEAwfPhwAcM011yA7OxsbN26EwWDAkiVLcP755+PgwYNITHT8h/jaa69FdnY2fvjhB9TV1WHs2LFITU3F3XffDQCorq7Geeedh6effhpXX3019u/fj7y8PHTv3h1jx46NzosnIiKiFpGU5nnabuyBrEv/i0m9MWtwNjqmJCpdt6n55MC+yZmx/8Xbm6I5HGplbp7cB4kmAx5asQt1XPaOSFdUM/aLFy/GggULlOsFBQUQQuDw4cPKbdu2bcP06dNhcP7He9asWThx4gR2794NANi+fTs+++wz3HPPPQCA5ORk/OpXv8JTTz0Fm83xxX/zzTdht9uV5+rfvz/mzJmDp59+OiKvk4iIiELPmJQKAEiRGjS36/TL8yBJEnp2bK9Z856aL8GoDewPl2hL8NnHgFpqQI5jCk29hYE9kZ6oBvZ5eXkwmRz/QbVYLHj22WcxePBgzJw5U9lm/vz5WL58Oerq6gAA7733HgwGAzp16gQAWLNmDVJSUjBgwADlMfn5+SgpKcH27duVbfLy8jRn8PPz87FmzZqwv0YiIiIKD3P7TABAJ1Sih3QGudIZSLAziIwCOWN/qrIeR0pr2eSMQi7Z2R2/usGK42frYLdzyQUitZhonnf77bcjKysLX3zxBVavXo2UlBTlvr///e/o2bMnunXrhp49e+K5557Dk08+idzcXADA4cOHkZ2drdlfTk4OAODIkSM+t6msrERZWZnHeBobG1FVVaX5R0RERLElKa0jAKCf4SS+TrwL6xPvwivmlzifOwrkjP0T/9mLac+u87ifR4RaSg7sS2saMeWZtbj9/S1RHhFRbImJwP6VV15BaWkpCgoKMHHiRJw6dUq574YbbsDx48dRWFiIo0eP4t1338XAgQOV++vq6pS59jL5upzlD2QbtSeffBLp6enKP/kkAhEREcWOHoPysc80ANWiHeqE47/rw6QjzNhHwQUjuiAz2YyURJPyjyiUenRIxvg+HdDO2RNj87HyKI+IKLbERGAPACaTCY8//jjsdjuef/55AI759UuXLsWjjz6KlJQUSJKEiy66CAsWLMCGDRsAOObUNzY2avYlX09OTg54G7Xf//73qKysVP4VFhaG9sUSERFRiyW1a48BD/6A1EWnUXv1pwAAo8T5t9Fw6aju2Prwedi5aLby76IRXZX7A2loSOSLyWjAh7dOwH/vmgIAqKi3RHlERLElqoF9U1OT5rrBYED//v2Vxnj79+8HAPTq1UvZJjExETk5OVi2bBkAoE+fPjhz5oxmP6dPn1bu87VNeno6OnTo4DGuxMREpKWlaf4RERFR7DIYHetcm2BnEBkjjIF0MSQKUnqy47veZLVj4EOrMOThz/HylweiPCqi6ItqYD969GiP206dOoWuXR1neLt166bcJrPZbCguLlYy7TNmzEBNTY1yEgAANm3ahM6dOytL5s2YMQNbtmzRrG2/adMmTZM+IiIiimMGR3muAfaAuuJT+BlUJ1h4roVCJTXRhIE5jhUxGix21DbZ8NlPp/w8iqj1i2pgv3v3bqxcuVK5vnTpUuzbtw/XX389AGDs2LEYMmQInnvuOdjtjuVTXn31VdTX12PevHkAgOHDh+PCCy/Ec889BwCor6/Ha6+9hvvuu09ZIu/GG2+EJEn48MMPAQAHDhzAqlWrsHDhwoi9ViIiIgofV8bexuZ5McIYMxM+qTWRJAmf3TEJ6xdOw5+vGgUAsDjjBKK2LKqdTZYsWYLFixfjySefhN3uKJ379NNPMWnSJACA2WzGypUrce+992LcuHHK0ngrVqxAXl6esp933nkHv/71rzFu3Dg0NTVh/vz5uOuuu5T7U1NTsXr1avzqV79STgy89dZbGDt2bGRfMBEREYWFZHD8RjDCDgEugxULNBn7KI6DWh+z0YDcDskornb0zLLa+J0nimpgf8cdd+COO+7wuU3Pnj3x0Ucf+dwmIyMDS5cu9bnNkCFD8NVXXwU9RiIiIop9BqOjFN8EGwR/48cEA+dEUJiZjY7PmNXGjD0Ri6SIiIgo/hkcpfhG2JivjxFGzrGnMJMbNFrs/NYTMbAnIiKiuOfK2Ns1zXIpetgVn8LN7GzkwIw9EQN7IiIiagUkZ/M8gyQAwR/5scCgSdMzyKfQMxnkUnyezCNiYE9ERERxz2BwtQ2ShC2KIyEZu+JTuMkZe3bFJ2JgT0RERK2A5CzFBwDYGdjHAq5jT+FmMjJjTyRjYE9ERERxT17HHgAkuzWKIyEZu+JTuJkMzjn2dsHeGtTmRXW5OyIiIqJQMBhdP2mmrr8K2JQUxdEQANxU04iLEhzrjPf6pD1gNvp5BFFwMu0CqxKqcUJ0Rm3tFKSkpEZ7SERRw8CeiIiI4p7RZMZpdEIOSpFWfQiojvaIKAtAllwbWh7NkVBrZQIwyAAMQiE2rH4L2ZNuqywSZwAAGeJJREFUwDnZDO6pbWJgT0RERHFPMhiQePsGnCzciq7p7aI9HAIgABwuqUVKognZaYnRHg61Urs+fBBDLDsxaceDGPBjdzy3YBwuGN412sMiijgG9kRERNQqZGZ1QWZWl2gPg5wkAH37RnsU1NrZJt4NrLsJAJAtlePX72/FoC5p6JuVEuWREUUWm+cREREREVFcGl4wH6dFBwBANsqRiCY8/u/dUR4VUeQxsCciIiIiorhVLbUHAHyc+Bh2Jd6Eroc+jvKIiCKPgT0REREREcWt0m4zlMsmyY4Lk7ZFbzBEUcLAnoiIiIiI4taEW5ag8f6T2DHldQBAtr04yiMiijwG9kREREREFNcSk9rDmOyYa28WTVEeDVHkMbAnIiIiIqK4ZzCZAQBGWKM8EqLIY2BPRERERERxz2hKAACYBAN7ansY2BMRERERUdwzKhl7W5RHQhR5DOyJiIiIiCjuyYG9qZWX4jdZ7RF7LptdoLi6AUKIiD0nNQ8DeyIiIiIiinuuUvzWm7Ff8sUBDH74c+wsqgz7c9ntAhe9vAFjF6/BzW9vCvvzUcuYoj0AIiIiIiKiljIoGfvgAvuqBgs++rEQ1Q1WpLUz44r8XKQkxmaY9MIX+wEAj322Gx/dNkG5vaiiHv/aWhTSbH6D1YZdJ6sAAGv3FeOF/+0P2b79Gdu7Ayb26xSx52sNYvMTS0REREREFASTORFA8HPsl35/DH/6fJ9y3SABN07sHdKxhVqTTRvAP/P5Xvxr28mwPZ9dAEvWHAjb/t0lmQ3Y9vB5SDIbI/ac8Y6BPRERERERxT2jyRHamCQ7hN0OyRDYrONTFQ3a65UNXrYMj7X7ivHgJzuRmmTC2zeNRXZakt/HWO3awP6kc8xT+2ehR4fkkI1NkoDkBBNqGyPXt+CjTYVosNgx4ck1MBqksD7Xh7eOR7/OqWF9jkhhYE9ERERERHFPztgDwJbP34TBGFiok360GLMNNWif6Ahg2x/ai62rfwjXMD1s3lOMoVU1QBWw9pPtGJDjPdCcbTgMADCfMWDr6sPK7f3OnkCmoQl39jwHg7ukhX3M4ZRTdBDbiyqBCJxfEfWDAbSOwF4SbHHoV1VVFdLT01FZWYm0tPj+ohARERERtUb1tdVo90z3aA+D4kjjLRuQ2G1YtIfhVTBxKDP2REREREQU99q1T8V3PW5FxqkNQT/WbDSgW0Y7FJXXw2KP3HJyMrtdwGiQ4C/j2mixw2K3o32CCZJblXo7sxE9O7ZHeIvXW5fEdinRHkLIMLAnIiIiIqJWYcJNz7To8f1CNA6iSOM69kRERERERERxjIE9ERERERERURxjYE9EREREREQUxxjYExEREREREcUxBvZEREREREREcYyBPREREREREVEcY2BPREREREREFMcY2BMRERERERHFMQb2RERERERERHGMgT0RERERERFRHGNgT0RERERERBTHGNgTERERERERxTEG9kRERERERERxjIE9ERERERERURxjYE9EREREREQUxxjYExEREREREcUxBvZEREREREREcYyBPREREREREVEcM0V7APFACAEAqKqqivJIiIiIiIiIqC2Q4085HvWFgX0AqqurAQC5ublRHgkRERERERG1JdXV1UhPT/e5jSQCCf/bOLvdjpMnTyI1NRWSJEV7OD5VVVUhNzcXhYWFSEtLi/ZwSAePUXzgcYoPPE6xj8coPvA4xQcep9jHYxQf4uU4CSFQXV2Nrl27wmDwPYueGfsAGAwGdO/ePdrDCEpaWlpMf0iJxyhe8DjFBx6n2MdjFB94nOIDj1Ps4zGKD/FwnPxl6mVsnkdEREREREQUxxjYExEREREREcUxBvatTGJiIh555BEkJiZGeyjkBY9RfOBxig88TrGPxyg+8DjFBx6n2MdjFB9a43Fi8zwiIiIiIiKiOMaMPREREREREVEcY2BPREREREREFMcY2BMRERERERHFMQb2rcgnn3yC/Px8TJ48GVOnTsWuXbuiPaRWrampCffffz9MJhOOHj3qcf9f/vIX5OXlYeLEiZg7dy6Kioo8Hn/nnXdizJgxyMvLw29+8xs0NTVptikqKsIFF1yAiRMnYvTo0Xj99dfD+ZJanY8++gjnnXceZsyYgfz8fFx22WWaYyWEwGOPPYbRo0dj7NixuOaaa1BZWanZR2VlJa699lqMHTsWo0ePxqJFi+DemmT37t0oKCjAlClTMGbMGCxfvjwSL69VWLFiBebMmYMZM2Zg0qRJGD16ND744APNNjxOseXll1+GJElYt26d5nb+zYu+Rx99FCNHjkRBQYHyb968ecr9/C7FjsOHD2P+/PmYNm0ahgwZgvHjx2PTpk0AeJxiwcCBAzXfo4KCAvTr1w9TpkxRtuHfvNjQ2NiIu+66CyNGjMDUqVMxbtw4fPLJJ8r9ber7JKhV2Lhxo0hNTRX79+8XQgjx9ttvi27duomqqqooj6x1OnLkiBg/fry47rrrBABx5MgRzf3Lli0TXbp0ESUlJUIIIRYtWiRGjhwpbDabss0dd9whZs+eLaxWq7BarWLmzJnijjvuUO632Wxi5MiR4o9//KMQQoji4mKRnZ0tli1bFv4X2EqYzWbx+eefCyEc7+e1114rBgwYIBoaGoQQQjz33HNi+PDhoq6uTgghxI033iguvPBCzT4uvPBCcfPNNwshhKitrRVDhgwRzz33nHJ/VVWV6Natm1i6dKkQQoh9+/aJlJQUsXHjxrC/vtZg9uzZ4u2331auf/rpp0KSJPHTTz8pt/E4xY6ioiLRo0cPAUCsXbtWuZ1/82LDI488ojku7vhdig3FxcWiV69e4quvvhJCCGGxWMS0adPEBx98IITgcYoFU6dO9bht/vz54uWXXxZC8G9eLHnwwQdFr169REVFhRBCiC1btoiEhASxbds2IUTb+j4xsG8lLr30UnHllVcq1202m8jOzhYvvfRSFEfVeu3YsUMcOHBArF27VjewHzVqlLj//vuV6xUVFcJkMolPP/1UCCFEaWmpJugUQoiVK1cKs9kszp49K4QQYsWKFcJsNovq6mplm3vvvVeMHj06jK+sdfn5z3+uuf7jjz8KAOLbb78VVqtVZGVliddff125f9euXQKA2L59uxBCiJ9++kkAEHv37lW2eeWVV0RWVpawWq1CCCGWLFkiunTpIux2u7LNZZddJubNmxfOl9ZqbNq0SVgsFuV6VVWVACA++eQTIYTgcYox8+bNE6+//rpHYM+/ebHBV2DP71Ls+N3vfieuuuoqzW0HDhwQRUVFPE4x4vDhw5rrZ8+eFWlpaaKsrEwIwb95seSCCy4Ql112mea2rKws8fzzz7e57xNL8VuJNWvWYMyYMcp1g8GAvLw8fPHFF1EcVes1dOhQ9OvXT/e+srIybN26VXM80tPT0b9/f+V4fP3117BYLJpt8vPzYbFY8NVXXwFwHNMBAwYgJSVFs82WLVtQXl4ejpfV6nz88cea60lJSQAcZVvbt29HSUmJ5hgMGjQI7du3V47TmjVrkJKSggEDBijb5Ofno6SkBNu3b1e2ycvLgyRJmm3WrFkTttfVmuTl5cFkMgEALBYLnn32WQwePBgzZ84EAB6nGPLZZ5/BbDZj9uzZmtv5Ny8+8LsUO5YvX64p6QaAfv36oWvXrjxOMaJ3796a6x988AHmzJmDzMxM/s2LMfPnz8f69etx/PhxAMDq1atRUlKC7OzsNvd9YmDfCpw9exZVVVXIzs7W3J6Tk4MjR45EaVRtl/ye+zoehw8fhslkQseOHZX7s7KyYDQaNdvo7UP9HBSc7777Dl27dsXEiRNx+PBhANrjJEkSsrOzgzoG3raprKxEWVlZ2F5La3P77bcjKysLX3zxBVavXq380OFxig21tbV44IEH8MILL3jcx795seWNN95AQUEBJk6ciOuvvx6HDh0CwO9SrKitrcWRI0dgs9lw9dVXY+LEiZg9ezZWrVoFgMcpVr311lu48cYbAfBvXqy54YYb8NBDD2H48OEYNGgQzj//fPz85z/H5Zdf3ua+TwzsW4G6ujoAQGJioub2xMRE5T6KnECOR11dHRISEjwem5CQoNlGbx/q56DANTY24plnnsHLL78Ms9kc8HHydwx4nELjlVdeQWlpqRKQnDp1CkDg3ycep/B66KGHcNttt6FLly4e9/FvXuzo0aMHRo0ahS+++ALr169H7969kZeXh6KiIn6XYkRFRQUAx3dq4cKF+Oabb7Bw4UJceOGF+N///sfjFIN2796N06dPY9asWQD4Ny/W/O1vf8NTTz2FzZs3Y8+ePdiyZQvGjx8Pg8HQ5r5PDOxbgeTkZACOwEWtsbFRuY8iJ5DjkZyc7NEZFXB0UFVvo7cP9XNQ4H75y1/iiiuuwKWXXgog8OPk7xjwOIWOyWTC448/Drvdjueffx4Aj1Ms2LJlCzZu3IjbbrtN937+zYsdN910E+666y6YTCYYDAY89NBDSEpKwquvvsrvUowwGo0AgAsvvBAjRowAAMyYMQPTp0/HkiVLeJxi0FtvvYXrrrsOBoMjbOLfvNghhMDChQvxy1/+En379gUAjBgxAv/5z3/wxBNPtLnvEwP7VqBjx45IT0/HmTNnNLefPn0affr0idKo2i75Pfd1PPr06QOr1YqzZ88q95eUlMBms2m20dsH4Dn3i3y7//77kZycjMcff1y5zdtxOnPmTEDHwN826enp6NChQ2hfSCvk/sPHYDCgf//+2L17NwAep1iwcuVK1NfXY/r06SgoKMCVV14JAPjtb3+LgoIC2O12APybF4uMRiN69eqFQ4cO8bsUI7KyspCYmIhu3bppbu/ZsyeOHDnC4xRjbDYb3nvvPaUMH+DvvFhSUlKC8vJy9OrVS3N77969sWzZsjb3fWJg30pMnz4dmzdvVq4LIbBlyxalARVFTmZmJkaNGqU5HlVVVdi/f79yPKZMmQKz2azZZtOmTTCbzUpDnRkzZmDfvn2oqanRbJOXl4fMzMwIvZr499RTT6GwsBAvv/wyAGDz5s3YvHkzhg8fjqysLM0x2LNnD2pra5XjNGPGDNTU1GD//v3KNps2bULnzp0xfPhwZZstW7Zo1jvdtGkTv3sBGj16tMdtp06dQteuXQGAxykGPPTQQ9iyZQvWrVuHdevW4cMPPwQAvPjii1i3bh3y8/P5Ny9G3HnnnR63nTx5Ej169OB3KUYYjUbNdCPZmTNneJxi0H//+1/07dtX0zCZv/NiR6dOnZCYmOjxfTp16hSSk5Pb3vcpih35KYQ2btwo0tLSxIEDB4QQQrz77rtcxz4CvC13t2zZMtG1a1dRWloqhBDi8ccf113fdM6cOcJmswmbzSbOO+88zfqmVqtVjBw5UjzxxBNCCCFKSkpETk4O1zcNwmuvvSaGDBkivvvuO/Hjjz+KH3/8UTzyyCPizTffFEI41jYdMWKEsrbpL37xC921TW+99VYhhBB1dXVi2LBhumubvv/++0IIIfbv3y9SU1Njbm3TWCVJkvj3v/+tXH/33XeFwWAQ69evV27jcYotR44c0V3Hnn/zoq9Xr15ixYoVyvW//vWvIikpSezZs0cIwe9SrFi9erXIzMwUx44dE0I4lt9KTEwUn332mRCCxymWXH755eKNN97wuJ1/82LHrbfeKgYMGKAsRbh582ZhNpvFiy++KIRoW98nBvatyPLly0VeXp6YNGmSmDJliti5c2e0h9RqNTY2iqlTp4oRI0YIAGLcuHEea6a/9tprYtSoUWLChAni/PPPF4WFhZr7GxoaxB133CFGjx4tRo8eLX7961+LhoYGzTaFhYVi7ty54txzzxWjRo0Sr776athfW2tRVVUlDAaDAODxTw7s7Xa7WLRokRg1apTIz88XCxYsEOXl5Zr9lJeXi6uvvlqMHTtWjBw5Ujz66KOadUyFEGLnzp1iypQpYtKkSSIvL4//UQ7CSy+9JCZMmCAmTpwoJkyYIM4991xNoC8Ej1MsufPOO8W4ceMEADFixAhxxRVXKPfxb170vffee2LatGli6tSpYsKECaKgoEBs2LBBuZ/fpdjx7rvvipEjR4pJkyaJ8ePHiw8//FC5j8cpNpSXl4uOHTtq1plX49+82FBbWyvuvfdeMWrUKDFx4kQxfPhw8dxzzynfh7b0fZKEUNUUEBEREREREVFc4Rx7IiIiIiIiojjGwJ6IiIiIiIgojjGwJyIiIiIiIopjDOyJiIiIiIiI4hgDeyIiIiIiIqI4xsCeiIiIiIiIKI4xsCciIiIiIiKKYwzsiYiIKO4JIXDy5MlmPbaoqCjEoyEiIoosBvZERERx5ocffkBBQQEkScLAgQPx2GOPAQAqKirw6KOPoqKiImJjOXr0KB599FGP2y+55BK88MILERlDbW0tLrnkEhw8eFD3/lmzZuGll14CACxYsAD33Xef5v677roL//rXv8I9TCIiorBhYE9ERBRnxo4di3Xr1gEA7r//fjz88MMAHIH9okWLIh7YL1q0yOP23r17IycnJyJjuOuuuzBlyhRMmTLF4z6r1YrvvvsOkyZNAgBs2LDBY7s33ngD9957Lw4fPhyR8RIREYWaKdoDICIiotYnUtn6PXv24B//+AdOnTqle//WrVthMBgwYsQIHD9+HEVFRZg4caJmm5SUFFx//fV4+OGHsXTp0kgMm4iIKKSYsSciImoFdu/ejSuvvBIAcOWVV6KgoACffPIJAKCmpga33HILRo0ahalTp+KSSy7B8ePHATgy2OPHj4ckSfj4449x8cUXo1+/fhg5ciQA4NVXX8W4ceMwbdo05OfnY/HixRBCAAC+/PJL/Pa3vwUAFBQUoKCgAN999x0WLlyIXr16oaCgQDPGd999FyNHjsS4ceMwatQofPzxx8p9N998M3JycnDdddfhvvvuw9SpUzFgwACsXr3a5+tevnw5xo8fj+TkZM3tv/3tb1FQUICLLroI6enpmDFjBiZPnoxOnTrhkksu8Rjb9OnT8dlnn6GpqSng95yIiChmCCIiIopLAMSbb76pXD9y5IgAII4cOaLZ7qqrrhJXXXWVsNlsQgghnnjiCTF48GBhtVo1j7vpppuEzWYT1dXVoqCgQAghRH5+vtixY4cQQoiamhoxfPhw8fbbbyv7Xrt2rdD7OfHII4+IqVOnKtdXr14tUlJSxN69e4UQQmzfvl0kJSWJb775Rtnm+uuvF5mZmWLPnj1CCCGWLFkievTo4fM9mDt3rrjtttu83j979mzx7LPPCiGEuOGGG8Q999yju93p06cFALF582afz0dERBSLmLEnIiJqxQ4fPowPP/wQd999NwwGx3/2b731VuzevVuZpy+77rrrYDAYkJKSgrVr1wIAPv74YwwdOhQA0L59e5x//vlYtWpV0ONYvHgxLrnkEgwYMAAAMGzYMMyePRtPPPGEZrtRo0Zh4MCBABxVAMePH0d5ebnX/Z45cwYdOnTwev+uXbswePBgAI6qBvmyu4yMDGV/RERE8YZz7ImIiFqxXbt2QQiBO++8E2azWbm9Z8+eKCkp0WzbvXt3j8efOHECv/nNb1BaWgqz2YyjR4+id+/eQY9j586dmD59uua2fv36acrxAaBr167K5dTUVABAVVUVMjMzdfdbWVkJk0n/50xVVRVOnDiBIUOGAAD27t2rXHYnvze+TiIQERHFKgb2REREbcDSpUv9BuRGo1Fz/dixY5g1axYee+wx3HPPPQCARx991CPTH0rqMUiSBADKnH49GRkZsFgsmtveeustvPXWWygrK0NSUhKuu+461NbWor6+Hr/73e9gNBrx4osvKn0EACj78JX9JyIiilUsxSciImol5FJ7ALDb7aitrVUy1Pv27dNs+/DDD2Pv3r0+97dp0ybU19fjiiuuUG5zby6nfk6r1Yr6+nrdfQ0dOtRjnflDhw5h2LBhPsfgT05ODsrKyjS33XDDDVi3bh2mT5+Oa665BuvWrcPll1+O2bNnY/369Vi3bp0mqAeg7CM7O7tF4yEiIooGBvZEREStRMeOHWEwGFBeXo5NmzbhhhtuQJ8+fXDVVVfhT3/6ExoaGgAA3377LZYtW4Z+/fr53N/AgQMhSRLWrFkDAKivr/eYX5+VlQXAUcK+fPlyPPzww7r7euCBB7BixQocOHAAgGOKwOeff44//OEPLXrNEydO9DhhIPv+++8xYcIEAMA333yjXNZz8OBBZGRkKP0EiIiI4kq0u/cRERFRcDZu3CimTp0qAIgBAwaIRYsWKfctXLhQDBkyRIwbN05s2LBBCCFEdXW1uPXWW8WAAQNEQUGBuPDCC8WBAweEEEJs3bpVjBs3TgAQ48aNE3/+8581z/X666+LXr16icmTJ4uf//znYv78+SI9PV0sWLBA2WbBggVi5MiRYsKECWLv3r3i3nvvFT179hTp6eli7ty5ynZvv/22GDFihBg7dqwYOXKk+Mc//qHcd+edd4rs7GyRnZ0t7r33XrF3717NuLZv3677Xuzfv1+kpqaK6upqze0NDQ0iISFB6bCflZUl1q5d6/U9ffjhh8X111/v410nIiKKXZIQPiauEREREcW4O++8E507d8YDDzzQrMdXVFQgPz8fX375JXJzc0M8OiIiovBjKT4RERHFtaeffhq7d+9WpgwE65ZbbsGrr77KoJ6IiOIWM/ZERETUKpw9exYdO3aM2OOIiIhiBQN7IiIiIiIiojjGUnwiIiIiIiKiOMbAnoiIiIiIiCiOMbAnIiIiIiIiimMM7ImIiIiIiIjiGAN7IiIiIiIiojjGwJ6IiIiIiIgojjGwJyIiIiIiIopjDOyJiIiIiIiI4tj/B49xZemNXJ96AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(figsize=(12, 6))\n",
    "result.plot_objectives(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "35137d49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best heuristic objective is 3780.\n",
      "This is 1.9% worse than the best known value 3709.\n"
     ]
    }
   ],
   "source": [
    "solution = result.best_state\n",
    "objective = solution.objective()\n",
    "pct_diff = 100 * (objective - DATA.bkv) / DATA.bkv\n",
    "\n",
    "print(f\"Best heuristic objective is {objective}.\")\n",
    "print(f\"This is {pct_diff:.1f}% worse than the best known value {DATA.bkv}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ab663489",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "name = \"ALNS with random & adjacent removal + greedy repair\"\n",
    "plot(result.best_state.schedule, name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6f4df42-124d-4c76-9ad9-c7c9ccfcf830",
   "metadata": {},
   "source": [
    "### Adding local search to greedy repair"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baaf095e-e377-43bd-a842-0844dd532339",
   "metadata": {},
   "source": [
    "Some ALNS heuristics include a local search after the repair step. This is also the case for the Iterated Greedy algorithm by [Ruiz and Stützle (2007)](https://www.sciencedirect.com/science/article/abs/pii/S0377221705008507?casa_token=V1QrcrUlY_QAAAAA:Vg3ADRaLM7-AaOu2DAo7P0sLatwnNF4LWEnB6K9OqjtQtYNqEW4YTnOAmIz227byWa2LEE3939s), which is a crossover between large neighborhood search and iterated local search. Iterated Greedy is one of the best performing metaheuristics for the PFSP.\n",
    "\n",
    "There are various ways to implement this. As explained in the [documentation](https://alns.readthedocs.io/en/latest/other%20single-trajectory%20heuristics.html#ils), one could implement a perturbation and local search operator and pass this to ALNS as destroy and repair operator, respectively. We do it differently: we make a new repair operator function that adds a local search step after the repair step is completed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "13e73142",
   "metadata": {},
   "outputs": [],
   "source": [
    "def greedy_repair_then_local_search(state: Solution, rnd_state, **kwargs):\n",
    "    \"\"\"\n",
    "    Greedily insert the unassigned jobs back into the schedule (using NEH\n",
    "    ordering). Apply local search afterwards.\n",
    "    \"\"\"\n",
    "    state = greedy_repair(state, rnd_state, **kwargs)\n",
    "    local_search(state, **kwargs)\n",
    "    return state\n",
    "\n",
    "\n",
    "def local_search(solution: Solution, **kwargs):\n",
    "    \"\"\"\n",
    "    Improves the current solution in-place using the insertion neighborhood.\n",
    "    A random job is selected and put in the best new position. This continues\n",
    "    until relocating any of the jobs does not lead to an improving move.\n",
    "    \"\"\"\n",
    "    improved = True\n",
    "\n",
    "    while improved:\n",
    "        improved = False\n",
    "        current = solution.objective()\n",
    "\n",
    "        for job in rnd.choice(\n",
    "            solution.schedule, len(solution.schedule), replace=False\n",
    "        ):\n",
    "            solution.remove(job)\n",
    "            solution.opt_insert(job)\n",
    "\n",
    "            if solution.objective() < current:\n",
    "                improved = True\n",
    "                current = solution.objective()\n",
    "                break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4228e899-6b60-4789-8a25-46a85fe1450a",
   "metadata": {},
   "outputs": [],
   "source": [
    "alns = ALNS(rnd.RandomState(SEED))\n",
    "\n",
    "alns.add_destroy_operator(random_removal)\n",
    "alns.add_destroy_operator(adjacent_removal)\n",
    "\n",
    "alns.add_repair_operator(greedy_repair_then_local_search)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "389f79d0-94ec-405e-b696-8a74d62d1069",
   "metadata": {},
   "outputs": [],
   "source": [
    "ITERS = 600  # fewer iterations because local search is expensive\n",
    "\n",
    "init = NEH(DATA.processing_times)\n",
    "select = AlphaUCB(\n",
    "    scores=[5, 2, 1, 0.5],\n",
    "    alpha=0.05,\n",
    "    num_destroy=len(alns.destroy_operators),\n",
    "    num_repair=len(alns.repair_operators),\n",
    ")\n",
    "accept = SimulatedAnnealing.autofit(init.objective(), 0.05, 0.50, ITERS)\n",
    "stop = MaxIterations(ITERS)\n",
    "\n",
    "result = alns.iterate(init, select, accept, stop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "97de15f6-3560-4c76-a5ad-c253b79d27a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(figsize=(12, 6))\n",
    "result.plot_objectives(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b3374c6f-3782-4f85-8b45-9bbfdf91ea88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best heuristic objective is 3775.\n",
      "This is 1.8% worse than the best known value 3709.\n"
     ]
    }
   ],
   "source": [
    "solution = result.best_state\n",
    "objective = solution.objective()\n",
    "pct_diff = 100 * (objective - DATA.bkv) / DATA.bkv\n",
    "\n",
    "print(f\"Best heuristic objective is {objective}.\")\n",
    "print(f\"This is {pct_diff:.1f}% worse than the best known value {DATA.bkv}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "95d33e4b-5ec4-4f2a-bb3c-a01be4eb11e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "name = \"ALNS with random & adjacent removal + greedy repair with local search\"\n",
    "plot(result.best_state.schedule, name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de4737b9-a3d7-4e41-87c6-2ecf14b4c87b",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Tuning the destroy rate\n",
    "Besides the selection scheme and criteria, `ALNS.iterate` can also take keyword-arguments. This can be helpful, for instance, when we want to tune parameters of the destroy/repair operators. In our case, we want to test for which values of `n_destroy` we obtain the best results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "77907e0b-a63e-464d-b311-dc4c111bb400",
   "metadata": {},
   "outputs": [],
   "source": [
    "alns = ALNS(rnd.RandomState(SEED))\n",
    "\n",
    "alns.add_destroy_operator(random_removal)\n",
    "alns.add_destroy_operator(adjacent_removal)\n",
    "\n",
    "alns.add_repair_operator(greedy_repair)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3f4509d1-4742-4215-836a-8fcb9a46dda5",
   "metadata": {},
   "outputs": [],
   "source": [
    "objectives = {}\n",
    "\n",
    "for n_remove in range(2, 10):\n",
    "    select = AlphaUCB(\n",
    "        scores=[5, 2, 1, 0.5],\n",
    "        alpha=0.05,\n",
    "        num_destroy=len(alns.destroy_operators),\n",
    "        num_repair=len(alns.repair_operators),\n",
    "    )\n",
    "    \n",
    "    iters = 2000 / (n_remove * 2)  # higher destroy rates use less iterations\n",
    "    accept = SimulatedAnnealing.autofit(init.objective(), 0.05, 0.50, iters)\n",
    "    stop = MaxRuntime(30)\n",
    "    result = alns.iterate(init, select, accept, stop, n_remove=n_remove)\n",
    "    objectives[n_remove] = result.best_state.objective()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e57a6cc4-5e2c-40ac-8c54-a366cafd5a4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=[8, 6])\n",
    "\n",
    "ax.plot(*zip(*sorted(objectives.items())))\n",
    "ax.set_title(\"Final objective value for various n_destroy values\")\n",
    "ax.set_ylabel(\"Objective value\")\n",
    "ax.set_xlabel(\"n_destroy\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d93bf45e-90fe-41ad-b78e-7aa7fdbe4bf6",
   "metadata": {},
   "source": [
    "From this simple experiment, it looks like removing between 2 to 6 jobs works best. Our experiment is clearly too simple to draw serious conclusions from, but this example can be easily extended to include more instances and also more random seeds."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b3ced50",
   "metadata": {},
   "source": [
    "## Conclusions\n",
    "In this notebook, we implemented several variants of adaptive large neighborhood search heuristic to solve the permutation flow shop problem. We obtained a solution that is only 1.5% worse than the best known solution. Furthermore, we showed several advanced features in ALNS, including:\n",
    "\n",
    "* Autofitting acceptance criteria\n",
    "* Adding local search to repair operators\n",
    "* Using the `**kwargs` argument in `ALNS.iterate` to tune the `n_remove` parameter in destroy operators\n",
    "\n"
   ]
  }
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